{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "TrainingDecoder.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JQyLDx53iP3X",
        "colab_type": "text"
      },
      "source": [
        "# Training on Mobility Data\n",
        "In this notebook we will train models on the mobility data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x86FdoN7iZaG",
        "colab_type": "code",
        "outputId": "d03a784a-f7d9-4a8e-bf6c-97eb36fad582",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "from google.colab import auth\n",
        "from datetime import datetime\n",
        "auth.authenticate_user()\n",
        "!wandb login\n",
        "!gcloud source repos clone github_aistream-peelout_flow-forecast --project=gmap-997\n",
        "os.chdir('/content/github_aistream-peelout_flow-forecast')\n",
        "!python setup.py develop\n",
        "!pip install -r requirements.txt\n",
        "!mkdir data\n",
        "from flood_forecast.trainer import train_function\n",
        "!pip install git+https://github.com/CoronaWhy/task-geo.git\n",
        "!pip install git+https://github.com/coronawhy/task-ts\n",
        "import wandb"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/bin/bash: wandb: command not found\n",
            "\u001b[1;33mWARNING:\u001b[0m Repository \"github_aistream-peelout_flow-forecast\" in project \"gmap-997\" is a mirror. Pushing to this clone will have no effect.  Instead, clone the mirrored repository directly with \n",
            "$ git clone https://github.com/AIStream-Peelout/flow-forecast\n",
            "Cloning into '/content/github_aistream-peelout_flow-forecast'...\n",
            "remote: Total 3943 (delta 2553), reused 3943 (delta 2553)\u001b[K\n",
            "Receiving objects: 100% (3943/3943), 2.71 MiB | 15.67 MiB/s, done.\n",
            "Resolving deltas: 100% (2553/2553), done.\n",
            "Project [gmap-997] repository [github_aistream-peelout_flow-forecast] was cloned to [/content/github_aistream-peelout_flow-forecast].\n",
            "/usr/local/lib/python3.6/dist-packages/setuptools/dist.py:454: UserWarning: Normalizing '0.01dev' to '0.1.dev0'\n",
            "  warnings.warn(tmpl.format(**locals()))\n",
            "running develop\n",
            "running egg_info\n",
            "creating flood_forecast.egg-info\n",
            "writing flood_forecast.egg-info/PKG-INFO\n",
            "writing dependency_links to flood_forecast.egg-info/dependency_links.txt\n",
            "writing requirements to flood_forecast.egg-info/requires.txt\n",
            "writing top-level names to flood_forecast.egg-info/top_level.txt\n",
            "writing manifest file 'flood_forecast.egg-info/SOURCES.txt'\n",
            "package init file 'flood_forecast/__init__.py' not found (or not a regular file)\n",
            "package init file 'flood_forecast/transformer_xl/__init__.py' not found (or not a regular file)\n",
            "package init file 'flood_forecast/preprocessing/__init__.py' not found (or not a regular file)\n",
            "package init file 'flood_forecast/da_rnn/__init__.py' not found (or not a regular file)\n",
            "package init file 'flood_forecast/basic/__init__.py' not found (or not a regular file)\n",
            "package init file 'flood_forecast/custom/__init__.py' not found (or not a regular file)\n",
            "writing manifest file 'flood_forecast.egg-info/SOURCES.txt'\n",
            "running build_ext\n",
            "Creating /usr/local/lib/python3.6/dist-packages/flood-forecast.egg-link (link to .)\n",
            "Adding flood-forecast 0.1.dev0 to easy-install.pth file\n",
            "\n",
            "Installed /content/github_aistream-peelout_flow-forecast\n",
            "Processing dependencies for flood-forecast==0.1.dev0\n",
            "Searching for google-cloud\n",
            "Reading https://pypi.org/simple/google-cloud/\n",
            "Downloading https://files.pythonhosted.org/packages/ba/b1/7c54d1950e7808df06642274e677dbcedba57f75307adf2e5ad8d39e5e0e/google_cloud-0.34.0-py2.py3-none-any.whl#sha256=fb1ab7b0548fe44b3d538041f0a374505b7f990d448a935ea36649c5ccab5acf\n",
            "Best match: google-cloud 0.34.0\n",
            "Processing google_cloud-0.34.0-py2.py3-none-any.whl\n",
            "Installing google_cloud-0.34.0-py2.py3-none-any.whl to /usr/local/lib/python3.6/dist-packages\n",
            "Adding google-cloud 0.34.0 to easy-install.pth file\n",
            "\n",
            "Installed /usr/local/lib/python3.6/dist-packages/google_cloud-0.34.0-py3.6.egg\n",
            "Searching for pandas==1.0.3\n",
            "Best match: pandas 1.0.3\n",
            "Adding pandas 1.0.3 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for tensorflow==2.2.0\n",
            "Best match: tensorflow 2.2.0\n",
            "Adding tensorflow 2.2.0 to easy-install.pth file\n",
            "Installing estimator_ckpt_converter script to /usr/local/bin\n",
            "Installing saved_model_cli script to /usr/local/bin\n",
            "Installing tensorboard script to /usr/local/bin\n",
            "Installing tf_upgrade_v2 script to /usr/local/bin\n",
            "Installing tflite_convert script to /usr/local/bin\n",
            "Installing toco script to /usr/local/bin\n",
            "Installing toco_from_protos script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for torch==1.5.0+cu101\n",
            "Best match: torch 1.5.0+cu101\n",
            "Adding torch 1.5.0+cu101 to easy-install.pth file\n",
            "Installing convert-caffe2-to-onnx script to /usr/local/bin\n",
            "Installing convert-onnx-to-caffe2 script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for scikit-learn==0.22.2.post1\n",
            "Best match: scikit-learn 0.22.2.post1\n",
            "Adding scikit-learn 0.22.2.post1 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for numpy==1.18.4\n",
            "Best match: numpy 1.18.4\n",
            "Adding numpy 1.18.4 to easy-install.pth file\n",
            "Installing f2py script to /usr/local/bin\n",
            "Installing f2py3 script to /usr/local/bin\n",
            "Installing f2py3.6 script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for python-dateutil==2.8.1\n",
            "Best match: python-dateutil 2.8.1\n",
            "Adding python-dateutil 2.8.1 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for pytz==2018.9\n",
            "Best match: pytz 2018.9\n",
            "Adding pytz 2018.9 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for termcolor==1.1.0\n",
            "Best match: termcolor 1.1.0\n",
            "Adding termcolor 1.1.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for opt-einsum==3.2.1\n",
            "Best match: opt-einsum 3.2.1\n",
            "Adding opt-einsum 3.2.1 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for absl-py==0.9.0\n",
            "Best match: absl-py 0.9.0\n",
            "Adding absl-py 0.9.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for wheel==0.34.2\n",
            "Best match: wheel 0.34.2\n",
            "Adding wheel 0.34.2 to easy-install.pth file\n",
            "Installing wheel script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for protobuf==3.10.0\n",
            "Best match: protobuf 3.10.0\n",
            "Adding protobuf 3.10.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for tensorflow-estimator==2.2.0\n",
            "Best match: tensorflow-estimator 2.2.0\n",
            "Adding tensorflow-estimator 2.2.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for h5py==2.10.0\n",
            "Best match: h5py 2.10.0\n",
            "Adding h5py 2.10.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for scipy==1.4.1\n",
            "Best match: scipy 1.4.1\n",
            "Adding scipy 1.4.1 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for wrapt==1.12.1\n",
            "Best match: wrapt 1.12.1\n",
            "Adding wrapt 1.12.1 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for astunparse==1.6.3\n",
            "Best match: astunparse 1.6.3\n",
            "Adding astunparse 1.6.3 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for six==1.12.0\n",
            "Best match: six 1.12.0\n",
            "Adding six 1.12.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for Keras-Preprocessing==1.1.2\n",
            "Best match: Keras-Preprocessing 1.1.2\n",
            "Adding Keras-Preprocessing 1.1.2 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for tensorboard==2.2.1\n",
            "Best match: tensorboard 2.2.1\n",
            "Adding tensorboard 2.2.1 to easy-install.pth file\n",
            "Installing tensorboard script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for grpcio==1.29.0\n",
            "Best match: grpcio 1.29.0\n",
            "Adding grpcio 1.29.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for gast==0.3.3\n",
            "Best match: gast 0.3.3\n",
            "Adding gast 0.3.3 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for google-pasta==0.2.0\n",
            "Best match: google-pasta 0.2.0\n",
            "Adding google-pasta 0.2.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for future==0.16.0\n",
            "Best match: future 0.16.0\n",
            "Adding future 0.16.0 to easy-install.pth file\n",
            "Installing futurize script to /usr/local/bin\n",
            "Installing pasteurize script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for joblib==0.15.1\n",
            "Best match: joblib 0.15.1\n",
            "Adding joblib 0.15.1 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for setuptools==46.4.0\n",
            "Best match: setuptools 46.4.0\n",
            "Adding setuptools 46.4.0 to easy-install.pth file\n",
            "Installing easy_install script to /usr/local/bin\n",
            "Installing easy_install-3.8 script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for Markdown==3.2.2\n",
            "Best match: Markdown 3.2.2\n",
            "Adding Markdown 3.2.2 to easy-install.pth file\n",
            "Installing markdown_py script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for tensorboard-plugin-wit==1.6.0.post3\n",
            "Best match: tensorboard-plugin-wit 1.6.0.post3\n",
            "Adding tensorboard-plugin-wit 1.6.0.post3 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for google-auth==1.7.2\n",
            "Best match: google-auth 1.7.2\n",
            "Adding google-auth 1.7.2 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for google-auth-oauthlib==0.4.1\n",
            "Best match: google-auth-oauthlib 0.4.1\n",
            "Adding google-auth-oauthlib 0.4.1 to easy-install.pth file\n",
            "Installing google-oauthlib-tool script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for requests==2.23.0\n",
            "Best match: requests 2.23.0\n",
            "Adding requests 2.23.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for Werkzeug==1.0.1\n",
            "Best match: Werkzeug 1.0.1\n",
            "Adding Werkzeug 1.0.1 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for importlib-metadata==1.6.0\n",
            "Best match: importlib-metadata 1.6.0\n",
            "Adding importlib-metadata 1.6.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for pyasn1-modules==0.2.8\n",
            "Best match: pyasn1-modules 0.2.8\n",
            "Adding pyasn1-modules 0.2.8 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for rsa==4.0\n",
            "Best match: rsa 4.0\n",
            "Adding rsa 4.0 to easy-install.pth file\n",
            "Installing pyrsa-decrypt script to /usr/local/bin\n",
            "Installing pyrsa-encrypt script to /usr/local/bin\n",
            "Installing pyrsa-keygen script to /usr/local/bin\n",
            "Installing pyrsa-priv2pub script to /usr/local/bin\n",
            "Installing pyrsa-sign script to /usr/local/bin\n",
            "Installing pyrsa-verify script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for cachetools==3.1.1\n",
            "Best match: cachetools 3.1.1\n",
            "Adding cachetools 3.1.1 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for requests-oauthlib==1.3.0\n",
            "Best match: requests-oauthlib 1.3.0\n",
            "Adding requests-oauthlib 1.3.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for urllib3==1.24.3\n",
            "Best match: urllib3 1.24.3\n",
            "Adding urllib3 1.24.3 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for chardet==3.0.4\n",
            "Best match: chardet 3.0.4\n",
            "Adding chardet 3.0.4 to easy-install.pth file\n",
            "Installing chardetect script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for idna==2.9\n",
            "Best match: idna 2.9\n",
            "Adding idna 2.9 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for certifi==2020.4.5.1\n",
            "Best match: certifi 2020.4.5.1\n",
            "Adding certifi 2020.4.5.1 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for zipp==3.1.0\n",
            "Best match: zipp 3.1.0\n",
            "Adding zipp 3.1.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for pyasn1==0.4.8\n",
            "Best match: pyasn1 0.4.8\n",
            "Adding pyasn1 0.4.8 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for oauthlib==3.1.0\n",
            "Best match: oauthlib 3.1.0\n",
            "Adding oauthlib 3.1.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Finished processing dependencies for flood-forecast==0.1.dev0\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 1)) (0.22.2.post1)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 2)) (1.0.3)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 3)) (1.5.0+cu101)\n",
            "Collecting tb-nightly\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/cd/85/17daa51272812331873571020632fc0f5300f0d448e51ec584a7bacc0fa7/tb_nightly-2.3.0a20200528-py3-none-any.whl (2.9MB)\n",
            "\u001b[K     |████████████████████████████████| 2.9MB 1.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: seaborn in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 5)) (0.10.1)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 6)) (0.16.0)\n",
            "Collecting wandb\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d1/c7/8bf2c62c3f133f45e135a8a116e4e0f162043248e3db54de30996eaf1a8a/wandb-0.8.36-py2.py3-none-any.whl (1.4MB)\n",
            "\u001b[K     |████████████████████████████████| 1.4MB 18.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas_gbq in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 8)) (0.11.0)\n",
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            "Requirement already satisfied: google-cloud-storage in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 10)) (1.18.1)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 11)) (3.13)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 12)) (4.4.1)\n",
            "Requirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->-r requirements.txt (line 1)) (1.4.1)\n",
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            "Requirement already satisfied: numpy>=1.11.0 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->-r requirements.txt (line 1)) (1.18.4)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->-r requirements.txt (line 2)) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas->-r requirements.txt (line 2)) (2.8.1)\n",
            "Requirement already satisfied: protobuf>=3.6.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly->-r requirements.txt (line 4)) (3.10.0)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly->-r requirements.txt (line 4)) (1.6.0.post3)\n",
            "Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.6/dist-packages (from tb-nightly->-r requirements.txt (line 4)) (0.9.0)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly->-r requirements.txt (line 4)) (2.23.0)\n",
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            "Requirement already satisfied: wheel>=0.26; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tb-nightly->-r requirements.txt (line 4)) (0.34.2)\n",
            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tb-nightly->-r requirements.txt (line 4)) (1.7.2)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/44/33/917e6fde1cad13daa7053f39b7c8af3be287314f75f1b1ea8d3fe37a8571/GitPython-3.1.2-py3-none-any.whl (451kB)\n",
            "\u001b[K     |████████████████████████████████| 460kB 15.3MB/s \n",
            "\u001b[?25hCollecting subprocess32>=3.5.3\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/32/c8/564be4d12629b912ea431f1a50eb8b3b9d00f1a0b1ceff17f266be190007/subprocess32-3.5.4.tar.gz (97kB)\n",
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            "\u001b[?25hCollecting watchdog>=0.8.3\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/73/c3/ed6d992006837e011baca89476a4bbffb0a91602432f73bd4473816c76e2/watchdog-0.10.2.tar.gz (95kB)\n",
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            "\u001b[?25hCollecting docker-pycreds>=0.4.0\n",
            "  Downloading https://files.pythonhosted.org/packages/f5/e8/f6bd1eee09314e7e6dee49cbe2c5e22314ccdb38db16c9fc72d2fa80d054/docker_pycreds-0.4.0-py2.py3-none-any.whl\n",
            "Collecting configparser>=3.8.1\n",
            "  Downloading https://files.pythonhosted.org/packages/4b/6b/01baa293090240cf0562cc5eccb69c6f5006282127f2b846fad011305c79/configparser-5.0.0-py3-none-any.whl\n",
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            "Collecting shortuuid>=0.5.0\n",
            "  Downloading https://files.pythonhosted.org/packages/25/a6/2ecc1daa6a304e7f1b216f0896b26156b78e7c38e1211e9b798b4716c53d/shortuuid-1.0.1-py3-none-any.whl\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1b/95/9a20eebcedab2c1c63fad59fe19a0469edfc2a25b8576497e8084629c2ff/sentry_sdk-0.14.4-py2.py3-none-any.whl (104kB)\n",
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            "\u001b[?25hCollecting gql==0.2.0\n",
            "  Downloading https://files.pythonhosted.org/packages/c4/6f/cf9a3056045518f06184e804bae89390eb706168349daa9dff8ac609962a/gql-0.2.0.tar.gz\n",
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            "Collecting gitdb<5,>=4.0.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/48/11/d1800bca0a3bae820b84b7d813ad1eff15a48a64caea9c823fc8c1b119e8/gitdb-4.0.5-py3-none-any.whl (63kB)\n",
            "\u001b[K     |████████████████████████████████| 71kB 8.9MB/s \n",
            "\u001b[?25hCollecting pathtools>=0.1.1\n",
            "  Downloading https://files.pythonhosted.org/packages/e7/7f/470d6fcdf23f9f3518f6b0b76be9df16dcc8630ad409947f8be2eb0ed13a/pathtools-0.1.2.tar.gz\n",
            "Collecting graphql-core<2,>=0.5.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b0/89/00ad5e07524d8c523b14d70c685e0299a8b0de6d0727e368c41b89b7ed0b/graphql-core-1.1.tar.gz (70kB)\n",
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            "Collecting smmap<4,>=3.0.1\n",
            "  Downloading https://files.pythonhosted.org/packages/b0/9a/4d409a6234eb940e6a78dfdfc66156e7522262f5f2fecca07dc55915952d/smmap-3.0.4-py2.py3-none-any.whl\n",
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            "Building wheels for collected packages: subprocess32, watchdog, gql, pathtools, graphql-core\n",
            "  Building wheel for subprocess32 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for subprocess32: filename=subprocess32-3.5.4-cp36-none-any.whl size=6489 sha256=280884442d07c60651c8f68f55c26b34ba44f7d807411e2be43187490e7c3c40\n",
            "  Stored in directory: /root/.cache/pip/wheels/68/39/1a/5e402bdfdf004af1786c8b853fd92f8c4a04f22aad179654d1\n",
            "  Building wheel for watchdog (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for watchdog: filename=watchdog-0.10.2-cp36-none-any.whl size=73605 sha256=48a49c4a1d738f6eb0135ce1fd14986d344660098beba8ef979530d4cfbacde8\n",
            "  Stored in directory: /root/.cache/pip/wheels/bc/ed/6c/028dea90d31b359cd2a7c8b0da4db80e41d24a59614154072e\n",
            "  Building wheel for gql (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gql: filename=gql-0.2.0-cp36-none-any.whl size=7630 sha256=38548a3896ff23d13edf364c5125fa4da8dfef43586b0bc0971fc8077a8cbb10\n",
            "  Stored in directory: /root/.cache/pip/wheels/ce/0e/7b/58a8a5268655b3ad74feef5aa97946f0addafb3cbb6bd2da23\n",
            "  Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pathtools: filename=pathtools-0.1.2-cp36-none-any.whl size=8784 sha256=3a2eb30a5b42227e673fc0f8b1098f1536659c38f7d04bbeb1a12fa11005067d\n",
            "  Stored in directory: /root/.cache/pip/wheels/0b/04/79/c3b0c3a0266a3cb4376da31e5bfe8bba0c489246968a68e843\n",
            "  Building wheel for graphql-core (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for graphql-core: filename=graphql_core-1.1-cp36-none-any.whl size=104650 sha256=a48d17e184fc63251279a4b06e80d7f46a23cd63a0e447b862166f0a1ae83a7e\n",
            "  Stored in directory: /root/.cache/pip/wheels/45/99/d7/c424029bb0fe910c63b68dbf2aa20d3283d023042521bcd7d5\n",
            "Successfully built subprocess32 watchdog gql pathtools graphql-core\n",
            "Installing collected packages: tb-nightly, smmap, gitdb, GitPython, subprocess32, pathtools, watchdog, docker-pycreds, configparser, shortuuid, sentry-sdk, graphql-core, gql, wandb\n",
            "Successfully installed GitPython-3.1.2 configparser-5.0.0 docker-pycreds-0.4.0 gitdb-4.0.5 gql-0.2.0 graphql-core-1.1 pathtools-0.1.2 sentry-sdk-0.14.4 shortuuid-1.0.1 smmap-3.0.4 subprocess32-3.5.4 tb-nightly-2.3.0a20200528 wandb-0.8.36 watchdog-0.10.2\n",
            "Collecting git+https://github.com/CoronaWhy/task-geo.git\n",
            "  Cloning https://github.com/CoronaWhy/task-geo.git to /tmp/pip-req-build-x2yg5c0b\n",
            "  Running command git clone -q https://github.com/CoronaWhy/task-geo.git /tmp/pip-req-build-x2yg5c0b\n",
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            "Collecting hdx-python-api\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/79/32/7033d6d9ff01fd592ec649756f78460dc66640c1001f39c2d421037866f3/hdx_python_api-4.5.8-py2.py3-none-any.whl (67kB)\n",
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            "Collecting ckanapi>=4.3\n",
            "  Downloading https://files.pythonhosted.org/packages/07/c6/50449e18aaf1600dfda955805c58aa7462493511f3ebbb20d0a65874397c/ckanapi-4.3.tar.gz\n",
            "Collecting pyOpenSSL\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9e/de/f8342b68fa9e981d348039954657bdf681b2ab93de27443be51865ffa310/pyOpenSSL-19.1.0-py2.py3-none-any.whl (53kB)\n",
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            "Collecting hdx-python-country>=2.5.6\n",
            "  Downloading https://files.pythonhosted.org/packages/24/45/684ec41e237b3e185c268b4ad4cd71581c3dfdb9c3e279d7777473edb8c2/hdx_python_country-2.5.6-py2.py3-none-any.whl\n",
            "Collecting ndg-httpsclient\n",
            "  Downloading https://files.pythonhosted.org/packages/fb/67/c2f508c00ed2a6911541494504b7cac16fe0b0473912568df65fd1801132/ndg_httpsclient-0.5.1-py3-none-any.whl\n",
            "Collecting quantulum3>=0.7.3; python_version >= \"3\"\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d0/d5/60d72e7c9393199eba72f88deb06e0fddc4e357d600bdbe5f86e8cfc4512/quantulum3-0.7.3-py3-none-any.whl (11.1MB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/3d/4e/e47c858f5a3bc3b21d76d4dd8a85cc1cf876ca8ab68fe734f37cb504a685/libhxl-4.20.tar.gz (83kB)\n",
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            "\u001b[?25hCollecting ratelimit\n",
            "  Downloading https://files.pythonhosted.org/packages/ab/38/ff60c8fc9e002d50d48822cc5095deb8ebbc5f91a6b8fdd9731c87a147c9/ratelimit-2.2.1.tar.gz\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d3/8a/a7ed55c2c55bd4f5844d72734fedc0cef8a74518a0a19105a21c15628f1e/psycopg2_binary-2.8.5-cp36-cp36m-manylinux1_x86_64.whl (2.9MB)\n",
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            "\u001b[?25hCollecting yamlloader\n",
            "  Downloading https://files.pythonhosted.org/packages/93/a2/2f0c2394af1559021703c8cbb1bc7419bb5a94ea6bde0ab8cd1e973bb605/yamlloader-0.5.5-py3-none-any.whl\n",
            "Collecting sshtunnel\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/c5/5c/4b320d7ec4b0d5d4d6df1fdf66a5799625b3623d0ce4efe81719c6f8dfb3/sshtunnel-0.1.5.tar.gz (49kB)\n",
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            "\u001b[?25hCollecting basicauth\n",
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            "Collecting colorlog\n",
            "  Downloading https://files.pythonhosted.org/packages/00/0d/22c73c2eccb21dd3498df7d22c0b1d4a30f5a5fb3feb64e1ce06bc247747/colorlog-4.1.0-py2.py3-none-any.whl\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/06/1e/1e08baaaf6c3d3df1459fd85f0e7d2d6aa916f33958f151ee1ecc9800971/paramiko-2.7.1-py2.py3-none-any.whl (206kB)\n",
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            "  Downloading https://files.pythonhosted.org/packages/82/e5/03207a0f11e1d60df85b97b61704ed701b725a7c2feaf83f7bfbd0c2d83e/linear-tsv-1.1.0.tar.gz\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/11/82/03c325c85196744658c6d095c1e90dbd408595c596fc136b2157b2edaa10/ijson-3.0.4-cp36-cp36m-manylinux1_x86_64.whl (98kB)\n",
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            "\u001b[?25hCollecting unicodecsv>=0.14\n",
            "  Downloading https://files.pythonhosted.org/packages/6f/a4/691ab63b17505a26096608cc309960b5a6bdf39e4ba1a793d5f9b1a53270/unicodecsv-0.14.1.tar.gz\n",
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            "\u001b[?25hCollecting dnspython>=1.15.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ec/d3/3aa0e7213ef72b8585747aa0e271a9523e713813b9a20177ebe1e939deb0/dnspython-1.16.0-py2.py3-none-any.whl (188kB)\n",
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            "  Stored in directory: /root/.cache/pip/wheels/a6/09/e9/e800279c98a0a8c94543f3de6c8a562f60e51363ed26e71283\n",
            "  Building wheel for openpyxl (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for openpyxl: filename=openpyxl-3.0.3-py2.py3-none-any.whl size=241262 sha256=bc743c505ce18b8da74d160118642b3594ad02e2d4d6ec736b9928f9c492b924\n",
            "  Stored in directory: /root/.cache/pip/wheels/b5/85/ca/e768ac132e57e75e645a151f8badac71cc0089e7225dddf76b\n",
            "Successfully built task-geo ckanapi libhxl python-io-wrapper jsonpath-ng ratelimit sshtunnel linear-tsv unicodecsv openpyxl\n",
            "\u001b[31mERROR: hdx-python-utilities 2.3.4 has requirement six>=1.14.0, but you'll have six 1.12.0 which is incompatible.\u001b[0m\n",
            "Installing collected packages: ckanapi, cryptography, pyOpenSSL, unidecode, python-io-wrapper, ply, jsonpath-ng, libhxl, ratelimit, psycopg2-binary, yamlloader, pynacl, bcrypt, paramiko, sshtunnel, basicauth, linear-tsv, ijson, unicodecsv, jsonlines, openpyxl, cchardet, tabulator, pyaml, dnspython, email-validator, colorlog, hdx-python-utilities, hdx-python-country, ndg-httpsclient, num2words, quantulum3, hdx-python-api, task-geo\n",
            "  Found existing installation: openpyxl 2.5.9\n",
            "    Uninstalling openpyxl-2.5.9:\n",
            "      Successfully uninstalled openpyxl-2.5.9\n",
            "Successfully installed basicauth-0.4.1 bcrypt-3.1.7 cchardet-2.1.6 ckanapi-4.3 colorlog-4.1.0 cryptography-2.9.2 dnspython-1.16.0 email-validator-1.1.1 hdx-python-api-4.5.8 hdx-python-country-2.5.6 hdx-python-utilities-2.3.4 ijson-3.0.4 jsonlines-1.2.0 jsonpath-ng-1.5.1 libhxl-4.20 linear-tsv-1.1.0 ndg-httpsclient-0.5.1 num2words-0.5.10 openpyxl-3.0.3 paramiko-2.7.1 ply-3.11 psycopg2-binary-2.8.5 pyOpenSSL-19.1.0 pyaml-20.4.0 pynacl-1.4.0 python-io-wrapper-0.1 quantulum3-0.7.3 ratelimit-2.2.1 sshtunnel-0.1.5 tabulator-1.49.2 task-geo-0.1.0.dev0 unicodecsv-0.14.1 unidecode-1.1.1 yamlloader-0.5.5\n",
            "Collecting git+https://github.com/coronawhy/task-ts\n",
            "  Cloning https://github.com/coronawhy/task-ts to /tmp/pip-req-build-qk07_tgz\n",
            "  Running command git clone -q https://github.com/coronawhy/task-ts /tmp/pip-req-build-qk07_tgz\n",
            "Collecting loguru\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/80/b0/4413a201fcdcdc6789050c536d3b4ece601975ded9e0d676ef47f582348d/loguru-0.5.0-py3-none-any.whl (56kB)\n",
            "\u001b[K     |████████████████████████████████| 61kB 1.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from corona-ts-tools==0.0.1) (1.0.3)\n",
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.6/dist-packages (from corona-ts-tools==0.0.1) (2.2.0)\n",
            "Collecting taskgeo@ git+https://github.com/CoronaWhy/task-geo.git\n",
            "  Cloning https://github.com/CoronaWhy/task-geo.git to /tmp/pip-install-o5lx2qws/taskgeo\n",
            "  Running command git clone -q https://github.com/CoronaWhy/task-geo.git /tmp/pip-install-o5lx2qws/taskgeo\n",
            "\u001b[33m  WARNING: Generating metadata for package taskgeo produced metadata for project name task-geo. Fix your #egg=taskgeo fragments.\u001b[0m\n",
            "Requirement already satisfied (use --upgrade to upgrade): task-geo from git+https://github.com/CoronaWhy/task-geo.git in /usr/local/lib/python3.6/dist-packages (from corona-ts-tools==0.0.1)\n",
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            "Collecting aiocontextvars>=0.2.0; python_version < \"3.7\"\n",
            "  Downloading https://files.pythonhosted.org/packages/db/c1/7a723e8d988de0a2e623927396e54b6831b68cb80dce468c945b849a9385/aiocontextvars-0.2.2-py2.py3-none-any.whl\n",
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            "Requirement already satisfied: scipy==1.4.1; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tensorflow->corona-ts-tools==0.0.1) (1.4.1)\n",
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            "Requirement already satisfied: jupyter in /usr/local/lib/python3.6/dist-packages (from task-geo->corona-ts-tools==0.0.1) (1.0.0)\n",
            "Requirement already satisfied: hdx-python-api in /usr/local/lib/python3.6/dist-packages (from task-geo->corona-ts-tools==0.0.1) (4.5.8)\n",
            "Collecting contextvars==2.4; python_version < \"3.7\"\n",
            "  Downloading https://files.pythonhosted.org/packages/83/96/55b82d9f13763be9d672622e1b8106c85acb83edd7cc2fa5bc67cd9877e9/contextvars-2.4.tar.gz\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow->corona-ts-tools==0.0.1) (3.2.2)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow->corona-ts-tools==0.0.1) (1.0.1)\n",
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            "Requirement already satisfied: quantulum3>=0.7.3; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from hdx-python-api->task-geo->corona-ts-tools==0.0.1) (0.7.3)\n",
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            "Collecting immutables>=0.9\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/99/e0/ea6fd4697120327d26773b5a84853f897a68e33d3f9376b00a8ff96e4f63/immutables-0.14-cp36-cp36m-manylinux1_x86_64.whl (98kB)\n",
            "\u001b[K     |████████████████████████████████| 102kB 3.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard<2.3.0,>=2.2.0->tensorflow->corona-ts-tools==0.0.1) (1.6.0)\n",
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            "Requirement already satisfied: docutils<0.16,>=0.10 in /usr/local/lib/python3.6/dist-packages (from botocore<1.17.0,>=1.16.13->boto3>=1.9->tabulator[cchardet]>=1.42.0->hdx-python-utilities>=2.3.4->hdx-python-country>=2.5.6->hdx-python-api->task-geo->corona-ts-tools==0.0.1) (0.15.2)\n",
            "Building wheels for collected packages: corona-ts-tools, task-geo, task-geo, contextvars\n",
            "  Building wheel for corona-ts-tools (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for corona-ts-tools: filename=corona_ts_tools-0.0.1-cp36-none-any.whl size=9369 sha256=7f1ce7611fd403214db6b7807e01c287024d28d7a91c52950df38838e684b5f5\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-7tmo09xq/wheels/84/8c/21/775765ae58f390facc7a50b890c997befead02d4a4e4871a8e\n",
            "  Building wheel for task-geo (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for task-geo: filename=task_geo-0.1.0.dev0-py2.py3-none-any.whl size=177679 sha256=7b55c117095197c6d0e375af32ffb4d66e3e0f439360936547c158e5307fb0fe\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-7tmo09xq/wheels/4f/c4/a4/33f04d80a745ae3ff088cae6f8cda3b46c48e007585d9ff0bf\n",
            "  Building wheel for task-geo (setup.py) ... \u001b[?25lerror\n",
            "\u001b[31m  ERROR: Failed building wheel for task-geo\u001b[0m\n",
            "\u001b[?25h  Running setup.py clean for task-geo\n",
            "\u001b[31m  ERROR: Failed cleaning build dir for task-geo\u001b[0m\n",
            "  Building wheel for contextvars (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for contextvars: filename=contextvars-2.4-cp36-none-any.whl size=7666 sha256=ebf80f5fb3ab35cfcfe717a8660d20791060447c69a1d0dd7cd10d9af0099558\n",
            "  Stored in directory: /root/.cache/pip/wheels/a5/7d/68/1ebae2668bda2228686e3c1cf16f2c2384cea6e9334ad5f6de\n",
            "Successfully built corona-ts-tools task-geo contextvars\n",
            "Failed to build task-geo\n",
            "\u001b[31mERROR: corona-ts-tools 0.0.1 requires taskgeo@ git+https://github.com/CoronaWhy/task-geo.git, which is not installed.\u001b[0m\n",
            "Installing collected packages: immutables, contextvars, aiocontextvars, loguru, corona-ts-tools\n",
            "Successfully installed aiocontextvars-0.2.2 contextvars-2.4 corona-ts-tools-0.0.1 immutables-0.14 loguru-0.5.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ickj7DUUijLI",
        "colab_type": "text"
      },
      "source": [
        "Now that we have all the basic things setup lets define our config file. Since mobility data has a longer lag we will need to define our parameter sweeps to have a longer lookback window."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dx-tmLRain2Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def make_config_file(file_path, df_len, weight_path=None):\n",
        "  run = wandb.init(project=\"covid_forecast\", entity=\"covid\")\n",
        "  wandb_config = wandb.config\n",
        "  train_number = df_len * .7\n",
        "  validation_number = df_len *.9\n",
        "  config_default={                 \n",
        "    \"model_name\": \"CustomTransformerDecoder\",\n",
        "    \"model_type\": \"PyTorch\",\n",
        "    \"model_params\": {\n",
        "        \"seq_length\":wandb_config[\"forecast_history\"],\n",
        "        \"n_time_series\":9,\n",
        "        \"output_seq_length\":wandb_config[\"out_seq_length\"],\n",
        "        \"n_layers_encoder\": wandb_config[\"number_encoder_layers\"],\n",
        "        \"use_mask\": wandb_config[\"use_mask\"]\n",
        "    },\n",
        "    \"dataset_params\":\n",
        "    {  \"class\": \"default\",\n",
        "       \"training_path\": file_path,\n",
        "       \"validation_path\": file_path,\n",
        "       \"test_path\": file_path,\n",
        "       \"batch_size\":wandb_config[\"batch_size\"],\n",
        "       \"forecast_history\":wandb_config[\"forecast_history\"],\n",
        "       \"forecast_length\":wandb_config[\"out_seq_length\"],\n",
        "       \"train_end\": int(train_number),\n",
        "       \"valid_start\":int(train_number+1),\n",
        "       \"valid_end\": int(validation_number),\n",
        "       \"target_col\": [\"new_cases\"],\n",
        "       \"relevant_cols\": [\"new_cases\", \"month\", \"weekday\", \"mobility_retail_recreation\",\t\"mobility_grocery_pharmacy\",\t\"mobility_parks\",\t\"mobility_transit_stations\",\t\"mobility_workplaces\",\t\"mobility_residential\"],\n",
        "       \"scaler\": \"StandardScaler\", \n",
        "       \"interpolate\": False\n",
        "    },\n",
        "    \"training_params\":\n",
        "    {\n",
        "       \"criterion\":\"MSE\",\n",
        "       \"optimizer\": \"Adam\",\n",
        "       \"optim_params\":\n",
        "       {\n",
        "\n",
        "       },\n",
        "       \"lr\": wandb_config[\"lr\"],\n",
        "       \"epochs\": 10,\n",
        "       \"batch_size\":wandb_config[\"batch_size\"]\n",
        "    \n",
        "    },\n",
        "    \"GCS\": True,\n",
        "    \"early_stopping\":\n",
        "    {\n",
        "        \"patience\":3\n",
        "    },\n",
        "    \"sweep\":True,\n",
        "    \"wandb\":False,\n",
        "    \"forward_params\":{},\n",
        "   \"metrics\":[\"MSE\"],\n",
        "   \"inference_params\":\n",
        "   {     \n",
        "         \"datetime_start\":\"2020-04-21\",\n",
        "          \"hours_to_forecast\":10, \n",
        "          \"test_csv_path\":file_path,\n",
        "          \"decoder_params\":{\n",
        "              \"decoder_function\": \"simple_decode\", \n",
        "            \"unsqueeze_dim\": 1\n",
        "          },\n",
        "          \"dataset_params\":{\n",
        "             \"file_path\": file_path,\n",
        "             \"forecast_history\":wandb_config[\"forecast_history\"],\n",
        "             \"forecast_length\":wandb_config[\"out_seq_length\"],\n",
        "             \"relevant_cols\": [\"new_cases\", \"month\", \"weekday\", \"mobility_retail_recreation\",\t\"mobility_grocery_pharmacy\",\t\"mobility_parks\",\t\"mobility_transit_stations\",\t\"mobility_workplaces\",\t\"mobility_residential\"],\n",
        "             \"target_col\": [\"new_cases\"],\n",
        "             \"scaling\": \"StandardScaler\",\n",
        "             \"interpolate_param\": False\n",
        "          }\n",
        "      }, \n",
        "      \"weight_path_add\":{\n",
        "      \"excluded_layers\":[\"out_length_lay.weight\", \"out_length_lay.bias\", \"dense_shape.weight\", \"dense_shape.bias\"]\n",
        "      }\n",
        "  }\n",
        "  if weight_path: \n",
        "    config_default[\"weight_path\"] = weight_path\n",
        "  wandb.config.update(config_default)\n",
        "  return config_default\n",
        "\n",
        "wandb_sweep_config_full = {\n",
        "  \"name\": \"Default sweep\",\n",
        "  \"method\": \"grid\",\n",
        "  \"parameters\": {\n",
        "        \"batch_size\": {\n",
        "            \"values\": [2, 5, 10, 20]\n",
        "        },\n",
        "        \"lr\":{\n",
        "            \"values\":[0.001, 0.002, 0.0001, .01]\n",
        "        },\n",
        "        \"forecast_history\":{\n",
        "            \"values\":[10, 11, 15]\n",
        "        },\n",
        "        \"out_seq_length\":{\n",
        "            \"values\":[1, 2, 3, 4, 5]\n",
        "        },\n",
        "        \"number_encoder_layers\":\n",
        "        {\n",
        "            \"values\":[1, 2, 3]\n",
        "        },\n",
        "        \"use_mask\":{\n",
        "            \"values\":[True, False]\n",
        "        }\n",
        "    }\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I1ZiZWvI0Bf5",
        "colab_type": "code",
        "outputId": "13981f18-158c-4902-bf2c-ef2a0c1af9d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        }
      },
      "source": [
        "os.environ['MODEL_BUCKET'] = \"coronaviruspublicdata\"\n",
        "os.environ[\"ENVIRONMENT_GCP\"] = \"Colab\"\n",
        "os.environ[\"GCP_PROJECT\"] = \"gmap-997\"\n",
        "!gsutil cp gs://predict_cfs/experiments/25_May_202010_29PM_model.pth .\n",
        "!gsutil cp gs://coronaviruspublicdata/experiments/26_May_202012_59AM_model.pth .\t\t"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Copying gs://predict_cfs/experiments/25_May_202010_29PM_model.pth...\n",
            "/ [1 files][  4.7 MiB/  4.7 MiB]                                                \n",
            "Operation completed over 1 objects/4.7 MiB.                                      \n",
            "Copying gs://coronaviruspublicdata/experiments/26_May_202012_59AM_model.pth...\n",
            "/ [1 files][  2.7 MiB/  2.7 MiB]                                                \n",
            "Operation completed over 1 objects/2.7 MiB.                                      \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "niQDvS8Wk8um",
        "colab_type": "text"
      },
      "source": [
        "## Loading the Data\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "va7HPKihk-cK",
        "colab_type": "code",
        "outputId": "4d914704-d676-4c69-cbf5-7aea0a5f0995",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "import glob\n",
        "from corona_ts.data_utils.data_crawler import load_data\n",
        "from corona_ts.data_utils.data_creator import loop_through_locations\n",
        "from corona_ts.data_utils.data_creator import region_df_format\n",
        "!mkdir dir /usr/local/lib/python3.6/dist-packages/data\n",
        "df = load_data()\n",
        "df['full_county'] = df['region'] + \"_\" + df['sub_region']\n",
        "important_cities_list = [\"United_States__California__Los_Angeles_County\", \"United_States__Illinois__Cook_County\", \"United_States__Arizona__Maricopa_County\", \"United_States__Massachusetts__Middlesex_County\", \"United_States__Texas__Dallas_County\", \"United_States__Texas__Harris_County\", \"United_States__Florida__Miami Dade_County\", \"United_States__California__Riverside_County\", \"United_States__Colorado__Denver_County\", \"United_States__Ohio__Cuyahoga_County\", \"United_States__New York__Queens_County\", \"United_States__New York__Bronx_County\"]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mkdir: cannot create directory ‘dir’: File exists\n",
            "mkdir: cannot create directory ‘/usr/local/lib/python3.6/dist-packages/data’: File exists\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2882: DtypeWarning: Columns (2) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
            "/usr/local/lib/python3.6/dist-packages/corona_ts/data_utils/data_crawler.py:150: DtypeWarning: Columns (3) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  mobility_df = fetch_mobility_data()\n",
            "2020-05-28 17:04:03.821 | INFO     | corona_ts.data_utils.data_crawler:_treat_mobility_missing_values:121 - Treat mobility missing values.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H5xICnbZx333",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#df_list = loop_through_locations(df)\n",
        "df['country'] = df['country'].str.replace(\" \",\"_\")\n",
        "df['sub_region'] = df['country'] +\"__\"+df['region'].str.replace(\" \", \"_\") + \"__\"+df['sub_region'].str.replace(\" \", \"_\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EMAcDy8ufyAX",
        "colab_type": "code",
        "outputId": "d2565533-7fb6-4579-e026-48e294f86a56",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "def loop_special_counties(special_counties_list):\n",
        "  for county in special_counties_list:\n",
        "    region = region_df_format(df,county)\n",
        "    file_path, len_df, file_path2 = format_corona_data(region, county)\n",
        "    sweep_id = wandb.sweep(wandb_sweep_config_full,'covid', 'covid_forecast')\n",
        "    wandb.agent(sweep_id, lambda: train_function(\"PyTorch\", make_config_file(file_path2, len(region), weight_path=\"25_May_202010_29PM_model.pth\")))\n",
        "loop_special_counties(important_cities_list)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:46: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "                 level        country      region  ... weekday month   datetime\n",
            "date                                               ...                         \n",
            "2020-02-15  sub_region  United_States  California  ...       5     2 2020-02-15\n",
            "2020-02-16  sub_region  United_States  California  ...       6     2 2020-02-16\n",
            "2020-02-17  sub_region  United_States  California  ...       0     2 2020-02-17\n",
            "2020-02-18  sub_region  United_States  California  ...       1     2 2020-02-18\n",
            "2020-02-19  sub_region  United_States  California  ...       2     2 2020-02-19\n",
            "2020-02-20  sub_region  United_States  California  ...       3     2 2020-02-20\n",
            "2020-02-21  sub_region  United_States  California  ...       4     2 2020-02-21\n",
            "2020-02-22  sub_region  United_States  California  ...       5     2 2020-02-22\n",
            "2020-02-23  sub_region  United_States  California  ...       6     2 2020-02-23\n",
            "\n",
            "[9 rows x 25 columns]\n",
            "Create sweep with ID: 65vo8egg\n",
            "Sweep URL: https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: []\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: ln8h14nh with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: ln8h14nh\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/ln8h14nh\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/ln8h14nh</a><br/>\n",
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            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
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          "metadata": {
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/ln8h14nh\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmxuOGgxNG5oOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170731-ln8h14nh/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170731-ln8h14nh/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170731-ln8h14nh/media/graph/graph_0_summary_eea37da3.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170731-ln8h14nh/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170731-ln8h14nh/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170731-ln8h14nh/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170731-ln8h14nh/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170731-ln8h14nh/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170731-ln8h14nh/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.545928395004012\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 0\n",
            "0.5552117283930004\n",
            "The running loss is:\n",
            "21.661523299873807\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 1\n",
            "0.7736258321383502\n",
            "The running loss is:\n",
            "14.07579830638133\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 2\n",
            "0.5027070823707618\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170731-ln8h14nh/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170731-ln8h14nh/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.572435699170455\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 3\n",
            "0.4133012749703734\n",
            "1\n",
            "The running loss is:\n",
            "11.783770642941818\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 4\n",
            "0.42084895153363633\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170731-ln8h14nh/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170731-ln8h14nh/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.61011728970334\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 5\n",
            "0.3432184746322621\n",
            "1\n",
            "The running loss is:\n",
            "13.171849727863446\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 6\n",
            "0.4704232045665516\n",
            "The running loss is:\n",
            "9.562567624612711\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 7\n",
            "0.34152027230759685\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170731-ln8h14nh/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170731-ln8h14nh/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.004484111152124\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 8\n",
            "0.3930172896840044\n",
            "2\n",
            "The running loss is:\n",
            "10.858391232584836\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 9\n",
            "0.38779968687802985\n",
            "Data saved to: \n",
            "28_May_202005_07PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.wandb_agent:Running runs: ['ln8h14nh']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_07PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170731-ln8h14nh/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170731-ln8h14nh/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170731-ln8h14nh/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  856.106934\n",
            "66  2020-04-22  sub_region  ...             66  898.349731\n",
            "67  2020-04-23  sub_region  ...             67  857.475891\n",
            "68  2020-04-24  sub_region  ...             68  861.468872\n",
            "69  2020-04-25  sub_region  ...             69  885.428162\n",
            "70  2020-04-26  sub_region  ...             70  897.812500\n",
            "71  2020-04-27  sub_region  ...             71  894.293274\n",
            "72  2020-04-28  sub_region  ...             72  929.067383\n",
            "73  2020-04-29  sub_region  ...             73  878.263550\n",
            "74  2020-04-30  sub_region  ...             74  869.549072\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170731-ln8h14nh/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170731-ln8h14nh/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: ln8h14nh \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170731-ln8h14nh/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170731-ln8h14nh/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170731-ln8h14nh/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170731-ln8h14nh/media/plotly/test_plot_20_314de4b9.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170731-ln8h14nh/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170731-ln8h14nh/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170731-ln8h14nh/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170731-ln8h14nh/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: ln8h14nh\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: n2zvztni with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: n2zvztni\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/n2zvztni\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/n2zvztni</a><br/>\n",
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        {
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/n2zvztni\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOm4yenZ6dG5pOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "15.36638443195261\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 0\n",
            "0.5487994439983075\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170746-n2zvztni/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170746-n2zvztni/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170746-n2zvztni/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170746-n2zvztni/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170746-n2zvztni/media/graph/graph_0_summary_fe13b12e.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170746-n2zvztni/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170746-n2zvztni/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170746-n2zvztni/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170746-n2zvztni/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.380546062253416\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 1\n",
            "0.6921623593661934\n",
            "The running loss is:\n",
            "13.36081693135202\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 2\n",
            "0.4771720332625721\n",
            "The running loss is:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170746-n2zvztni/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170746-n2zvztni/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "11.953934513032436\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 3\n",
            "0.4269262326083013\n",
            "1\n",
            "The running loss is:\n",
            "12.163511937658768\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 4\n",
            "0.43441114063067027\n",
            "The running loss is:\n",
            "9.378338906506542\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 5\n",
            "0.33494067523237653\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170746-n2zvztni/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170746-n2zvztni/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.704894061491359\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 6\n",
            "0.4537462164818343\n",
            "The running loss is:\n",
            "11.437989893951453\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 7\n",
            "0.4084996390696948\n",
            "1\n",
            "The running loss is:\n",
            "11.367679933318868\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 8\n",
            "0.40598856904710245\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170746-n2zvztni/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170746-n2zvztni/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.42585400538519\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 9\n",
            "0.40806621447804253\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_07PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_07PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['n2zvztni']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170746-n2zvztni/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170746-n2zvztni/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170746-n2zvztni/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  802.753906\n",
            "66  2020-04-22  sub_region  ...             66  877.084839\n",
            "67  2020-04-23  sub_region  ...             67  830.681274\n",
            "68  2020-04-24  sub_region  ...             68  816.030273\n",
            "69  2020-04-25  sub_region  ...             69  821.921509\n",
            "70  2020-04-26  sub_region  ...             70  826.182373\n",
            "71  2020-04-27  sub_region  ...             71  846.830322\n",
            "72  2020-04-28  sub_region  ...             72  856.710266\n",
            "73  2020-04-29  sub_region  ...             73  857.412720\n",
            "74  2020-04-30  sub_region  ...             74  841.012085\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170746-n2zvztni/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170746-n2zvztni/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170746-n2zvztni/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170746-n2zvztni/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170746-n2zvztni/media/plotly/test_plot_20_e30096eb.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170746-n2zvztni/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170746-n2zvztni/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: n2zvztni \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170746-n2zvztni/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170746-n2zvztni/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170746-n2zvztni/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170746-n2zvztni/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170746-n2zvztni/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170746-n2zvztni/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: n2zvztni\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: l1s6ccr7 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: l1s6ccr7\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/l1s6ccr7\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/l1s6ccr7</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/l1s6ccr7\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmwxczZjY3I3OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "10.473457891494036\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.38790584783311244\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170806-l1s6ccr7/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170806-l1s6ccr7/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170806-l1s6ccr7/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170806-l1s6ccr7/media/graph/graph_0_summary_081c54c1.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170806-l1s6ccr7/requirements.txt\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170806-l1s6ccr7/wandb-summary.json\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.50141921825707\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.4630155266021137\n",
            "1\n",
            "The running loss is:\n",
            "12.783501834142953\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.47346303089418346\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170806-l1s6ccr7/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170806-l1s6ccr7/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.422599470242858\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.20083701741640214\n",
            "1\n",
            "The running loss is:\n",
            "5.399664411786944\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.19998757080692384\n",
            "The running loss is:\n",
            "4.413807349279523\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.16347434626961196\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170806-l1s6ccr7/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170806-l1s6ccr7/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.564219214487821\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.16904515609214152\n",
            "The running loss is:\n",
            "4.552914118394256\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.16862644882941688\n",
            "1\n",
            "The running loss is:\n",
            "4.2418790506199\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.15710663150444074\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170806-l1s6ccr7/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170806-l1s6ccr7/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.331287162844092\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.16041804306829968\n",
            "Data saved to: \n",
            "28_May_202005_08PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_08PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['l1s6ccr7']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  839.469238\n",
            "66  2020-04-22  sub_region  ...             66  751.845520\n",
            "67  2020-04-23  sub_region  ...             67  796.254761\n",
            "68  2020-04-24  sub_region  ...             68  840.047058\n",
            "69  2020-04-25  sub_region  ...             69  856.757812\n",
            "70  2020-04-26  sub_region  ...             70  857.516724\n",
            "71  2020-04-27  sub_region  ...             71  805.222778\n",
            "72  2020-04-28  sub_region  ...             72  846.005188\n",
            "73  2020-04-29  sub_region  ...             73  746.322815\n",
            "74  2020-04-30  sub_region  ...             74  799.226501\n",
            "\n",
            "[20 rows x 28 columns]"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170806-l1s6ccr7/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170806-l1s6ccr7/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170806-l1s6ccr7/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170806-l1s6ccr7/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170806-l1s6ccr7/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170806-l1s6ccr7/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170806-l1s6ccr7/media/plotly/test_plot_20_908a465b.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170806-l1s6ccr7/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170806-l1s6ccr7/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: l1s6ccr7 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170806-l1s6ccr7/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170806-l1s6ccr7/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170806-l1s6ccr7/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170806-l1s6ccr7/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170806-l1s6ccr7/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170806-l1s6ccr7/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: l1s6ccr7\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: a2slkabz with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: a2slkabz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/a2slkabz\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/a2slkabz</a><br/>\n",
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/a2slkabz\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170816-a2slkabz/wandb-metadata.json\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmEyc2xrYWJ6OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170816-a2slkabz/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170816-a2slkabz/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170816-a2slkabz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170816-a2slkabz/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170816-a2slkabz/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170816-a2slkabz/media/graph/graph_0_summary_cb39fdfd.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170816-a2slkabz/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170816-a2slkabz/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.56319503299892\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.39122944566662665\n",
            "The running loss is:\n",
            "12.365171766839921\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.45796932469777485\n",
            "1\n",
            "The running loss is:\n",
            "10.063402196858078\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.37271859988363254\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170816-a2slkabz/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170816-a2slkabz/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.191733076237142\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.19228641023100526\n",
            "1\n",
            "The running loss is:\n",
            "5.048446481116116\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.1869794993005969\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170816-a2slkabz/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170816-a2slkabz/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.6334264650940895\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.17160838759607738\n",
            "1\n",
            "The running loss is:\n",
            "4.809368921909481\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.17812477488553635\n",
            "The running loss is:\n",
            "4.469904407858849\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.16555201510588327\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['a2slkabz']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170816-a2slkabz/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170816-a2slkabz/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.4312631585635245\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.164120857724575\n",
            "The running loss is:\n",
            "4.365964470198378\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.1617023877851251\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_08PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_08PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170816-a2slkabz/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170816-a2slkabz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170816-a2slkabz/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  820.661011\n",
            "66  2020-04-22  sub_region  ...             66  731.493591\n",
            "67  2020-04-23  sub_region  ...             67  777.978394\n",
            "68  2020-04-24  sub_region  ...             68  817.230530\n",
            "69  2020-04-25  sub_region  ...             69  833.734131\n",
            "70  2020-04-26  sub_region  ...             70  830.960205\n",
            "71  2020-04-27  sub_region  ...             71  781.981934\n",
            "72  2020-04-28  sub_region  ...             72  821.894836\n",
            "73  2020-04-29  sub_region  ...             73  732.720215\n",
            "74  2020-04-30  sub_region  ...             74  782.010132\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170816-a2slkabz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170816-a2slkabz/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170816-a2slkabz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170816-a2slkabz/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170816-a2slkabz/media/plotly/test_plot_20_a6760719.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170816-a2slkabz/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170816-a2slkabz/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: a2slkabz \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170816-a2slkabz/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170816-a2slkabz/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170816-a2slkabz/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170816-a2slkabz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170816-a2slkabz/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170816-a2slkabz/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: a2slkabz\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: jgun01i3 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: jgun01i3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/jgun01i3\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/jgun01i3</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/jgun01i3\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmpndW4wMWkzOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "22.41447990387678\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.8301659223658068\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170836-jgun01i3/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170836-jgun01i3/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170836-jgun01i3/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170836-jgun01i3/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170836-jgun01i3/media/graph/graph_0_summary_7ebd0d1d.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170836-jgun01i3/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170836-jgun01i3/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170836-jgun01i3/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170836-jgun01i3/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.35543255507946\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.7539049094473874\n",
            "1\n",
            "The running loss is:\n",
            "19.27651271224022\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7139449152681563\n",
            "The running loss is:\n",
            "12.351722501218319\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.4574712037488266\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170836-jgun01i3/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170836-jgun01i3/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "9.813126027584076\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.36344911213274356\n",
            "The running loss is:\n",
            "6.200845863670111\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.2296609579137078\n",
            "1\n",
            "The running loss is:\n",
            "5.99026171118021\n",
            "The number of items in train is: \n",
            "27\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170836-jgun01i3/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The loss for epoch 6\n",
            "0.2218615448585263\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170836-jgun01i3/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.51112406142056\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.20411570597853926\n",
            "1\n",
            "The running loss is:\n",
            "5.962472440674901\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.22083231261758893\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170836-jgun01i3/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170836-jgun01i3/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.207641955465078\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.1928756279801881\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_08PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_08PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['jgun01i3']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170836-jgun01i3/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170836-jgun01i3/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170836-jgun01i3/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  731.740784\n",
            "66  2020-04-22  sub_region  ...             66  738.606201\n",
            "67  2020-04-23  sub_region  ...             67  737.777405\n",
            "68  2020-04-24  sub_region  ...             68  726.210022\n",
            "69  2020-04-25  sub_region  ...             69  719.908691\n",
            "70  2020-04-26  sub_region  ...             70  719.150635\n",
            "71  2020-04-27  sub_region  ...             71  720.189941\n",
            "72  2020-04-28  sub_region  ...             72  731.834473\n",
            "73  2020-04-29  sub_region  ...             73  735.288940\n",
            "74  2020-04-30  sub_region  ...             74  731.156189\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170836-jgun01i3/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170836-jgun01i3/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170836-jgun01i3/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170836-jgun01i3/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170836-jgun01i3/media/plotly/test_plot_20_3857be08.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170836-jgun01i3/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170836-jgun01i3/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: jgun01i3 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170836-jgun01i3/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170836-jgun01i3/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170836-jgun01i3/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170836-jgun01i3/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170836-jgun01i3/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170836-jgun01i3/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: jgun01i3\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 8ont2i62 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 8ont2i62\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/8ont2i62\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/8ont2i62</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/8ont2i62\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjhvbnQyaTYyOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "22.289740599691868\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.8255459481367359\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170846-8ont2i62/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170846-8ont2i62/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170846-8ont2i62/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170846-8ont2i62/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170846-8ont2i62/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170846-8ont2i62/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170846-8ont2i62/media/graph/graph_0_summary_6ce8dcfc.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170846-8ont2i62/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170846-8ont2i62/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.721937209367752\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.7674791559025094\n",
            "1\n",
            "The running loss is:\n",
            "19.501514554023743\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7222783168156942\n",
            "The running loss is:\n",
            "13.022847175598145\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.4823276731703017\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170846-8ont2i62/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170846-8ont2i62/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "9.215275160968304\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.3413064874432705\n",
            "2\n",
            "The running loss is:\n",
            "6.453644126653671\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.23902385654272856\n",
            "The running loss is:\n",
            "6.3195782247930765\n",
            "The number of items in train is: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170846-8ont2i62/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "27\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170846-8ont2i62/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The loss for epoch 6\n",
            "0.23405845277011395\n",
            "1\n",
            "The running loss is:\n",
            "5.538634759373963\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.20513462071755417\n",
            "The running loss is:\n",
            "6.0211376789957285\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.22300509922206402\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170846-8ont2i62/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170846-8ont2i62/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.4065384501591325\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.2002421648207086\n",
            "Data saved to: \n",
            "28_May_202005_08PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_08PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['8ont2i62']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170846-8ont2i62/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170846-8ont2i62/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170846-8ont2i62/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  723.085754\n",
            "66  2020-04-22  sub_region  ...             66  731.862305\n",
            "67  2020-04-23  sub_region  ...             67  732.741089\n",
            "68  2020-04-24  sub_region  ...             68  723.526733\n",
            "69  2020-04-25  sub_region  ...             69  715.763306\n",
            "70  2020-04-26  sub_region  ...             70  713.154541\n",
            "71  2020-04-27  sub_region  ...             71  712.077576\n",
            "72  2020-04-28  sub_region  ...             72  725.158813\n",
            "73  2020-04-29  sub_region  ...             73  729.568176\n",
            "74  2020-04-30  sub_region  ...             74  726.045166\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170846-8ont2i62/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170846-8ont2i62/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170846-8ont2i62/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170846-8ont2i62/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170846-8ont2i62/media/plotly/test_plot_20_967a32da.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170846-8ont2i62/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170846-8ont2i62/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 8ont2i62 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170846-8ont2i62/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170846-8ont2i62/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170846-8ont2i62/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170846-8ont2i62/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170846-8ont2i62/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170846-8ont2i62/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 8ont2i62\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 9aousxvh with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 9aousxvh\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/9aousxvh\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/9aousxvh</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/9aousxvh\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjlhb3VzeHZoOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "17.69617347419262\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.6806220566997161\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170901-9aousxvh/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170901-9aousxvh/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170901-9aousxvh/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170901-9aousxvh/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170901-9aousxvh/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170901-9aousxvh/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170901-9aousxvh/media/graph/graph_0_summary_412005fa.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170901-9aousxvh/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170901-9aousxvh/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.942448504269123\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.4977864809334278\n",
            "1\n",
            "The running loss is:\n",
            "11.007499657571316\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.4233653714450506\n",
            "2\n",
            "The running loss is:\n",
            "7.731042757630348\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.297347798370398\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170901-9aousxvh/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170901-9aousxvh/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.677496463060379\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.1799037101177069\n",
            "1\n",
            "The running loss is:\n",
            "3.537908681668341\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.13607341083339775\n",
            "The running loss is:\n",
            "3.6070269970223308\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.13873180757778195\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170901-9aousxvh/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170901-9aousxvh/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "5.469477290287614\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.21036451116490823\n",
            "2\n",
            "The running loss is:\n",
            "4.373710255138576\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.16821962519763753\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170901-9aousxvh/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170901-9aousxvh/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.385507521219552\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.168673366200752\n",
            "Data saved to: \n",
            "28_May_202005_09PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_09PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['9aousxvh']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  641.062012\n",
            "66  2020-04-22  sub_region  ...             66  649.469788\n",
            "67  2020-04-23  sub_region  ...             67  662.655884\n",
            "68  2020-04-24  sub_region  ...             68  663.000610\n",
            "69  2020-04-25  sub_region  ...             69  652.813110\n",
            "70  2020-04-26  sub_region  ...             70  647.735291\n",
            "71  2020-04-27  sub_region  ...             71  634.131409\n",
            "72  2020-04-28  sub_region  ...             72  641.305664\n",
            "73  2020-04-29  sub_region  ...             73  641.875549\n",
            "74  2020-04-30  sub_region  ...             74  656.309570\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170901-9aousxvh/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170901-9aousxvh/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170901-9aousxvh/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170901-9aousxvh/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170901-9aousxvh/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170901-9aousxvh/media/plotly/test_plot_20_7320596b.plotly.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170901-9aousxvh/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170901-9aousxvh/media/plotly\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 9aousxvh \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170901-9aousxvh/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170901-9aousxvh/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170901-9aousxvh/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170901-9aousxvh/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170901-9aousxvh/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170901-9aousxvh/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 9aousxvh\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: rb6loxpb with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: rb6loxpb\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
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              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/rb6loxpb\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/rb6loxpb</a><br/>\n",
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          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
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          "output_type": "stream",
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "17.936274334788322\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.6898567051841662\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170912-rb6loxpb/config.yaml\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170912-rb6loxpb/wandb-summary.json\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170912-rb6loxpb/wandb-metadata.json\n",
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          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.563346169888973\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.48320562191880667\n",
            "1\n",
            "The running loss is:\n",
            "10.63600341975689\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.4090770546060342\n",
            "2\n",
            "The running loss is:\n",
            "7.032686905935407\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.27048795792059255\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170912-rb6loxpb/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170912-rb6loxpb/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.1291360929608345\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.1588129266523398\n",
            "1\n",
            "The running loss is:\n",
            "3.5215289955958724\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.13544342290753356\n",
            "The running loss is:\n",
            "4.55407845415175\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.17515686362122113\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170912-rb6loxpb/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170912-rb6loxpb/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "3.83646054379642\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.14755617476140076\n",
            "2\n",
            "The running loss is:\n",
            "4.684140918310732\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.18015926608887428\n",
            "The running loss is:\n",
            "7.284909620881081\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.28018883157234925\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170912-rb6loxpb/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170912-rb6loxpb/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "Data saved to: \n",
            "28_May_202005_09PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_09PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['rb6loxpb']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170912-rb6loxpb/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170912-rb6loxpb/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170912-rb6loxpb/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  811.324219\n",
            "66  2020-04-22  sub_region  ...             66  801.534241\n",
            "67  2020-04-23  sub_region  ...             67  837.117920\n",
            "68  2020-04-24  sub_region  ...             68  854.320984\n",
            "69  2020-04-25  sub_region  ...             69  834.952698\n",
            "70  2020-04-26  sub_region  ...             70  831.260376\n",
            "71  2020-04-27  sub_region  ...             71  782.874146\n",
            "72  2020-04-28  sub_region  ...             72  832.120972\n",
            "73  2020-04-29  sub_region  ...             73  785.114258\n",
            "74  2020-04-30  sub_region  ...             74  817.378418\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
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          "output_type": "stream",
          "text": [
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170912-rb6loxpb/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170912-rb6loxpb/config.yaml\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170912-rb6loxpb/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170912-rb6loxpb/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170912-rb6loxpb/media/plotly/test_plot_20_d2368e1b.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170912-rb6loxpb/media\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170912-rb6loxpb/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: rb6loxpb \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170912-rb6loxpb/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170912-rb6loxpb/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170912-rb6loxpb/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170912-rb6loxpb/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170912-rb6loxpb/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170912-rb6loxpb/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: rb6loxpb\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Network error resolved after 0:00:11.417233, resuming normal operation.\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: rbsc7bkb with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: rbsc7bkb\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/rbsc7bkb\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/rbsc7bkb</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/rbsc7bkb\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170933-rbsc7bkb/wandb-metadata.json\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnJic2M3YmtiOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['rbsc7bkb']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170933-rbsc7bkb/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.377284348011017\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.6298955518465775\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170933-rbsc7bkb/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170933-rbsc7bkb/media/graph/graph_0_summary_2fdadaf1.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170933-rbsc7bkb/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170933-rbsc7bkb/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170933-rbsc7bkb/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170933-rbsc7bkb/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170933-rbsc7bkb/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.166790530085564\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.5448765588494447\n",
            "The running loss is:\n",
            "9.428598899394274\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.3626384192074721\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170933-rbsc7bkb/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170933-rbsc7bkb/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.6330753564834595\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.2551182829416715\n",
            "2\n",
            "The running loss is:\n",
            "5.67338194232434\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.2182069977817054\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_09PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_09PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170933-rbsc7bkb/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170933-rbsc7bkb/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170933-rbsc7bkb/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  339.937469\n",
            "66  2020-04-22  sub_region  ...             66  348.662567\n",
            "67  2020-04-23  sub_region  ...             67  321.414612\n",
            "68  2020-04-24  sub_region  ...             68  302.783997\n",
            "69  2020-04-25  sub_region  ...             69  324.099854\n",
            "70  2020-04-26  sub_region  ...             70  317.279297\n",
            "71  2020-04-27  sub_region  ...             71  371.108948\n",
            "72  2020-04-28  sub_region  ...             72  297.625122\n",
            "73  2020-04-29  sub_region  ...             73  384.647125\n",
            "74  2020-04-30  sub_region  ...             74  332.773010\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170933-rbsc7bkb/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170933-rbsc7bkb/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: rbsc7bkb \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170933-rbsc7bkb/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170933-rbsc7bkb/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170933-rbsc7bkb/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170933-rbsc7bkb/media/plotly/test_plot_all_11_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170933-rbsc7bkb/media/plotly/test_plot_10_807ad6e3.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170933-rbsc7bkb/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170933-rbsc7bkb/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170933-rbsc7bkb/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: rbsc7bkb\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: tc6k32xx with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: tc6k32xx\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/tc6k32xx\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/tc6k32xx</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/tc6k32xx\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnRjNmszMnh4OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170949-tc6k32xx/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170949-tc6k32xx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170949-tc6k32xx/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170949-tc6k32xx/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170949-tc6k32xx/media/graph/graph_0_summary_cdca1200.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170949-tc6k32xx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170949-tc6k32xx/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170949-tc6k32xx/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170949-tc6k32xx/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.114518700167537\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 0\n",
            "0.5755185250059834\n",
            "The running loss is:\n",
            "40.93923968449235\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 1\n",
            "1.462115703017584\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170949-tc6k32xx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170949-tc6k32xx/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.72650630073622\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 2\n",
            "0.6330895107405793\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170949-tc6k32xx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170949-tc6k32xx/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.82603452168405\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 3\n",
            "0.4937869472030018\n",
            "The running loss is:\n",
            "12.235369089990854\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 4\n",
            "0.43697746749967337\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170949-tc6k32xx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170949-tc6k32xx/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.42129836557433\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 5\n",
            "0.4436177987705118\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['tc6k32xx']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.593332045944408\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 6\n",
            "0.4140475730694431\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170949-tc6k32xx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170949-tc6k32xx/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.022986069088802\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 7\n",
            "0.42939235961031436\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170949-tc6k32xx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170949-tc6k32xx/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.050711158488411\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 8\n",
            "0.3946682556603004\n",
            "2\n",
            "The running loss is:\n",
            "10.95676115965398\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 9\n",
            "0.3913128985590707\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_09PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170949-tc6k32xx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170949-tc6k32xx/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_09PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  837.403687\n",
            "66  2020-04-22  sub_region  ...             66  830.898865\n",
            "67  2020-04-23  sub_region  ...             67  824.932678\n",
            "68  2020-04-24  sub_region  ...             68  836.498291\n",
            "69  2020-04-25  sub_region  ...             69  841.817627\n",
            "70  2020-04-26  sub_region  ...             70  846.289368\n",
            "71  2020-04-27  sub_region  ...             71  830.351929\n",
            "72  2020-04-28  sub_region  ...             72  852.223999\n",
            "73  2020-04-29  sub_region  ...             73  823.185059\n",
            "74  2020-04-30  sub_region  ...             74  832.774414\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170949-tc6k32xx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170949-tc6k32xx/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170949-tc6k32xx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170949-tc6k32xx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170949-tc6k32xx/media/plotly/test_plot_20_754d0a08.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170949-tc6k32xx/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170949-tc6k32xx/media/plotly\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: tc6k32xx \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170949-tc6k32xx/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170949-tc6k32xx/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170949-tc6k32xx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170949-tc6k32xx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170949-tc6k32xx/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_170949-tc6k32xx/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: tc6k32xx\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 7bw2bkzf with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 7bw2bkzf\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/7bw2bkzf\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/7bw2bkzf</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/7bw2bkzf\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjdidzJia3pmOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171004-7bw2bkzf/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171004-7bw2bkzf/media/graph/graph_0_summary_0bbaab27.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171004-7bw2bkzf/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171004-7bw2bkzf/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171004-7bw2bkzf/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171004-7bw2bkzf/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171004-7bw2bkzf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171004-7bw2bkzf/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171004-7bw2bkzf/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.39689282909967\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 0\n",
            "0.5498890296107025\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171004-7bw2bkzf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171004-7bw2bkzf/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "44.767726235091686\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 1\n",
            "1.5988473655389888\n",
            "The running loss is:\n",
            "22.245736518874764\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 2\n",
            "0.794490589959813\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171004-7bw2bkzf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171004-7bw2bkzf/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.605747589841485\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 3\n",
            "0.5573481282086244\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171004-7bw2bkzf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171004-7bw2bkzf/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.130321642383933\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 4\n",
            "0.6475114872279976\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_10PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_10PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['7bw2bkzf']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  410.513519\n",
            "66  2020-04-22  sub_region  ...             66  402.391907\n",
            "67  2020-04-23  sub_region  ...             67  409.347412\n",
            "68  2020-04-24  sub_region  ...             68  416.087708\n",
            "69  2020-04-25  sub_region  ...             69  416.266327\n",
            "70  2020-04-26  sub_region  ...             70  415.997223\n",
            "71  2020-04-27  sub_region  ...             71  405.508575\n",
            "72  2020-04-28  sub_region  ...             72  415.981689\n",
            "73  2020-04-29  sub_region  ...             73  393.018555\n",
            "74  2020-04-30  sub_region  ...             74  405.671448\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171004-7bw2bkzf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171004-7bw2bkzf/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171004-7bw2bkzf/wandb-history.jsonl\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171004-7bw2bkzf/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171004-7bw2bkzf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171004-7bw2bkzf/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171004-7bw2bkzf/media/plotly/test_plot_10_d4fb56b9.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171004-7bw2bkzf/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171004-7bw2bkzf/media/plotly\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 7bw2bkzf \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171004-7bw2bkzf/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171004-7bw2bkzf/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171004-7bw2bkzf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171004-7bw2bkzf/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171004-7bw2bkzf/media/plotly/test_plot_all_11_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171004-7bw2bkzf/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 7bw2bkzf\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: tl51mwpo with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: tl51mwpo\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/tl51mwpo\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/tl51mwpo</a><br/>\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/tl51mwpo\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
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            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnRsNTFtd3BvOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
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          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171015-tl51mwpo/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171015-tl51mwpo/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171015-tl51mwpo/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171015-tl51mwpo/media/graph/graph_0_summary_6324096e.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171015-tl51mwpo/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171015-tl51mwpo/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171015-tl51mwpo/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171015-tl51mwpo/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171015-tl51mwpo/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.718430198729038\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.3969788962492236\n",
            "The running loss is:\n",
            "28.698574017733335\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.0629101488049384\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171015-tl51mwpo/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171015-tl51mwpo/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.018407717347145\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.5932743599017462\n",
            "1\n",
            "The running loss is:\n",
            "10.534781254827976\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.39017708351214725\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171015-tl51mwpo/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171015-tl51mwpo/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "2\n",
            "The running loss is:\n",
            "5.385364955291152\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.1994579613070797\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171015-tl51mwpo/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171015-tl51mwpo/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.625352939590812\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.28242047924410413\n",
            "1\n",
            "The running loss is:\n",
            "6.7238668041536584\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.2490321038575429\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['tl51mwpo']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171015-tl51mwpo/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171015-tl51mwpo/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.8772421116009355\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.29174970783707166\n",
            "The running loss is:\n",
            "7.317102946341038\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.27100381282744584\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171015-tl51mwpo/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171015-tl51mwpo/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "5.420254968106747\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.20075018400395359\n",
            "Data saved to: \n",
            "28_May_202005_10PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171015-tl51mwpo/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171015-tl51mwpo/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_10PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  779.043823\n",
            "66  2020-04-22  sub_region  ...             66  778.755005\n",
            "67  2020-04-23  sub_region  ...             67  778.881592\n",
            "68  2020-04-24  sub_region  ...             68  779.025024\n",
            "69  2020-04-25  sub_region  ...             69  779.112366\n",
            "70  2020-04-26  sub_region  ...             70  779.083008\n",
            "71  2020-04-27  sub_region  ...             71  778.987671\n",
            "72  2020-04-28  sub_region  ...             72  778.948608\n",
            "73  2020-04-29  sub_region  ...             73  778.723877\n",
            "74  2020-04-30  sub_region  ...             74  778.891602\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171015-tl51mwpo/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171015-tl51mwpo/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: tl51mwpo \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171015-tl51mwpo/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171015-tl51mwpo/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171015-tl51mwpo/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171015-tl51mwpo/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171015-tl51mwpo/media/plotly/test_plot_20_03adc1c1.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171015-tl51mwpo/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171015-tl51mwpo/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171015-tl51mwpo/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: tl51mwpo\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: c16hxf83 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: c16hxf83\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/c16hxf83\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/c16hxf83</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/c16hxf83\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmMxNmh4ZjgzOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171030-c16hxf83/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171030-c16hxf83/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171030-c16hxf83/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171030-c16hxf83/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171030-c16hxf83/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171030-c16hxf83/media/graph/graph_0_summary_4c786c3f.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171030-c16hxf83/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171030-c16hxf83/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171030-c16hxf83/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.668751269578934\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.3951389359103309\n",
            "The running loss is:\n",
            "28.521158926188946\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.0563392194884795\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171030-c16hxf83/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171030-c16hxf83/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.176970317959785\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.5250729747392513\n",
            "The running loss is:\n",
            "6.591888493858278\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.24414401829104732\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171030-c16hxf83/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171030-c16hxf83/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "7.487370473332703\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.27731001753084084\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171030-c16hxf83/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171030-c16hxf83/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.26125293970108\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.1948612199889289\n",
            "The running loss is:\n",
            "6.058157247491181\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.22437619435152523\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['c16hxf83']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171030-c16hxf83/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171030-c16hxf83/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.886977478861809\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.18099916588377069\n",
            "The running loss is:\n",
            "8.049873136915267\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.29814344951538024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171030-c16hxf83/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171030-c16hxf83/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "6.706313902512193\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.24838199638934047\n",
            "2\n",
            "Data saved to: \n",
            "28_May_202005_10PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171030-c16hxf83/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171030-c16hxf83/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_10PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  755.005249\n",
            "66  2020-04-22  sub_region  ...             66  751.443115\n",
            "67  2020-04-23  sub_region  ...             67  753.844849\n",
            "68  2020-04-24  sub_region  ...             68  755.624390\n",
            "69  2020-04-25  sub_region  ...             69  755.841370\n",
            "70  2020-04-26  sub_region  ...             70  755.883606\n",
            "71  2020-04-27  sub_region  ...             71  752.995850\n",
            "72  2020-04-28  sub_region  ...             72  755.255066\n",
            "73  2020-04-29  sub_region  ...             73  751.328247\n",
            "74  2020-04-30  sub_region  ...             74  753.881714\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171030-c16hxf83/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171030-c16hxf83/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171030-c16hxf83/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171030-c16hxf83/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171030-c16hxf83/media/plotly/test_plot_20_9c0dfd36.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171030-c16hxf83/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171030-c16hxf83/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: c16hxf83 \n",
            "\n"
          ],
          "name": "stdout"
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171030-c16hxf83/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171030-c16hxf83/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171030-c16hxf83/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171030-c16hxf83/media/plotly\n",
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            "INFO:wandb.wandb_agent:Cleaning up finished run: c16hxf83\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
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          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: vllrz9kx with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: vllrz9kx\n"
          ],
          "name": "stdout"
        },
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          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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          "name": "stderr"
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        {
          "output_type": "display_data",
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/vllrz9kx\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/vllrz9kx</a><br/>\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/vllrz9kx\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnZsbHJ6OWt4OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
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          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171046-vllrz9kx/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171046-vllrz9kx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171046-vllrz9kx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171046-vllrz9kx/requirements.txt\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171046-vllrz9kx/wandb-metadata.json\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171046-vllrz9kx/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "23.849574618041515\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.883317578445982\n",
            "The running loss is:\n",
            "20.568836465477943\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.7618087579806646\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171046-vllrz9kx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171046-vllrz9kx/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.27038937062025\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7137181248377871\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171046-vllrz9kx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171046-vllrz9kx/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.25364562869072\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.6390239121737303\n",
            "1\n",
            "The running loss is:\n",
            "14.78194186091423\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.5474793281820085\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171046-vllrz9kx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171046-vllrz9kx/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.99204146116972\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.40711264670998965\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['vllrz9kx']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "34.4778664149344\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "1.2769580153679405\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171046-vllrz9kx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171046-vllrz9kx/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.340780273079872\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.6792881582622174\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171046-vllrz9kx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171046-vllrz9kx/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.76011623442173\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.6948191197933974\n",
            "2\n",
            "The running loss is:\n",
            "18.743604812771082\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.6942075856581882\n",
            "Data saved to: \n",
            "28_May_202005_10PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171046-vllrz9kx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171046-vllrz9kx/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_10PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  351.287323\n",
            "66  2020-04-22  sub_region  ...             66  351.352722\n",
            "67  2020-04-23  sub_region  ...             67  351.261353\n",
            "68  2020-04-24  sub_region  ...             68  351.227020\n",
            "69  2020-04-25  sub_region  ...             69  351.256317\n",
            "70  2020-04-26  sub_region  ...             70  351.258667\n",
            "71  2020-04-27  sub_region  ...             71  351.388000\n",
            "72  2020-04-28  sub_region  ...             72  351.195374\n",
            "73  2020-04-29  sub_region  ...             73  351.413818\n",
            "74  2020-04-30  sub_region  ...             74  351.286987\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171046-vllrz9kx/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171046-vllrz9kx/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171046-vllrz9kx/wandb-summary.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171046-vllrz9kx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171046-vllrz9kx/media/plotly/test_plot_20_3234c249.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171046-vllrz9kx/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171046-vllrz9kx/media/plotly\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: vllrz9kx \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171046-vllrz9kx/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171046-vllrz9kx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171046-vllrz9kx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171046-vllrz9kx/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171046-vllrz9kx/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171046-vllrz9kx/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: vllrz9kx\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 8hosk45y with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 8hosk45y\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/8hosk45y\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/8hosk45y</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/8hosk45y\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjhob3NrNDV5OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/media/graph/graph_0_summary_08442c6e.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "23.785254307091236\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.8809353447070828\n",
            "The running loss is:\n",
            "20.365187123417854\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.7542661897562168\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.407820589840412\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7188081699940894\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.047366067767143\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.594346891398783\n",
            "1\n",
            "The running loss is:\n",
            "16.049381844699383\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.5944215498036809\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.695411691442132\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.3220522848682271\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['8hosk45y']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.989098764955997\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.4440406949983703\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.975145380944014\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.48056094003496347\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.981494817882776\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.25857388214380655\n",
            "The running loss is:\n",
            "7.491051498800516\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.27744635180742655\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_11PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_11PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  709.696960\n",
            "66  2020-04-22  sub_region  ...             66  709.161499\n",
            "67  2020-04-23  sub_region  ...             67  708.643188\n",
            "68  2020-04-24  sub_region  ...             68  708.597656\n",
            "69  2020-04-25  sub_region  ...             69  708.908020\n",
            "70  2020-04-26  sub_region  ...             70  708.755493\n",
            "71  2020-04-27  sub_region  ...             71  709.836182\n",
            "72  2020-04-28  sub_region  ...             72  708.476990\n",
            "73  2020-04-29  sub_region  ...             73  709.191528\n",
            "74  2020-04-30  sub_region  ...             74  708.709473\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/media/plotly/test_plot_20_509b02c0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 8hosk45y \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171103-8hosk45y/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 8hosk45y\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: x293l7uz with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: x293l7uz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/x293l7uz\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/x293l7uz</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/x293l7uz\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOngyOTNsN3V6OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171119-x293l7uz/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171119-x293l7uz/media/graph/graph_0_summary_4b05e63a.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171119-x293l7uz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171119-x293l7uz/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171119-x293l7uz/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171119-x293l7uz/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171119-x293l7uz/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171119-x293l7uz/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171119-x293l7uz/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "25.31017677485943\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.9734683374945934\n",
            "The running loss is:\n",
            "17.691114902496338\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.6804274962498591\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171119-x293l7uz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171119-x293l7uz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.284134477376938\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.8186205568221899\n",
            "The running loss is:\n",
            "14.811767756938934\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.5696833752668821\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171119-x293l7uz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171119-x293l7uz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.015214286744595\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.46212362641325366\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171119-x293l7uz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171119-x293l7uz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.353260762989521\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.3982023370380585\n",
            "2\n",
            "The running loss is:\n",
            "7.667264841496944\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.2948948015960363\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_11PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.wandb_agent:Running runs: ['x293l7uz']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171119-x293l7uz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171119-x293l7uz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_11PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  318.850616\n",
            "66  2020-04-22  sub_region  ...             66  316.706177\n",
            "67  2020-04-23  sub_region  ...             67  320.013977\n",
            "68  2020-04-24  sub_region  ...             68  321.565765\n",
            "69  2020-04-25  sub_region  ...             69  321.352051\n",
            "70  2020-04-26  sub_region  ...             70  320.382446\n",
            "71  2020-04-27  sub_region  ...             71  318.602081\n",
            "72  2020-04-28  sub_region  ...             72  318.317017\n",
            "73  2020-04-29  sub_region  ...             73  318.346680\n",
            "74  2020-04-30  sub_region  ...             74  320.484741\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171119-x293l7uz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171119-x293l7uz/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: x293l7uz \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171119-x293l7uz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171119-x293l7uz/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171119-x293l7uz/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171119-x293l7uz/media/plotly/test_plot_all_15_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171119-x293l7uz/media/plotly/test_plot_14_b1c54b64.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171119-x293l7uz/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171119-x293l7uz/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171119-x293l7uz/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: x293l7uz\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: tozkkomi with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: tozkkomi\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/tozkkomi\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/tozkkomi</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/tozkkomi\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnRvemtrb21pOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "25.090075597167015\n",
            "The number of items in train is: \n",
            "26\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The loss for epoch 0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/wandb-events.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0.9650029075833467\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/media/graph/graph_0_summary_38ea3612.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.45224541425705\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.6712402082406558\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.34785506129265\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.8210713485112557\n",
            "The running loss is:\n",
            "14.617094554007053\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.5621959443848866\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.857044279575348\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.4945017030605903\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.908861950039864\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.4195716134630717\n",
            "1\n",
            "The running loss is:\n",
            "7.671748608350754\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.2950672541673367\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['tozkkomi']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.437005430459976\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.2475771319407683\n",
            "The running loss is:\n",
            "7.865247845649719\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.3025095325249892\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.2042678743600845\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.2001641490138494\n",
            "Data saved to: \n",
            "28_May_202005_11PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_11PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  570.512756\n",
            "66  2020-04-22  sub_region  ...             66  571.143372\n",
            "67  2020-04-23  sub_region  ...             67  570.903015\n",
            "68  2020-04-24  sub_region  ...             68  570.691162\n",
            "69  2020-04-25  sub_region  ...             69  570.595093\n",
            "70  2020-04-26  sub_region  ...             70  570.591003\n",
            "71  2020-04-27  sub_region  ...             71  570.901611\n",
            "72  2020-04-28  sub_region  ...             72  570.663696\n",
            "73  2020-04-29  sub_region  ...             73  571.259827\n",
            "74  2020-04-30  sub_region  ...             74  571.026001\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/media/plotly/test_plot_20_96e49c04.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: tozkkomi \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171134-tozkkomi/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: tozkkomi\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: b7f459dg with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: b7f459dg\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/b7f459dg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/b7f459dg</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/b7f459dg\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmI3ZjQ1OWRnOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171151-b7f459dg/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171151-b7f459dg/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171151-b7f459dg/media/graph/graph_0_summary_939c93f5.graph.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "24.998789221048355\n",
            "The number of items in train is: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171151-b7f459dg/requirements.txt\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "26\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171151-b7f459dg/wandb-metadata.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The loss for epoch 0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171151-b7f459dg/wandb-events.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0.9614918931172445\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171151-b7f459dg/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171151-b7f459dg/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171151-b7f459dg/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.62556444108486\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.754829401580187\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171151-b7f459dg/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171151-b7f459dg/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.021592140197754\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.7315996976999136\n",
            "The running loss is:\n",
            "13.53062754869461\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.5204087518728696\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171151-b7f459dg/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171151-b7f459dg/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.939093425869942\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.45919590099499774\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171151-b7f459dg/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171151-b7f459dg/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.370361641049385\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.475783140040361\n",
            "1\n",
            "The running loss is:\n",
            "10.105389013886452\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.388668808226402\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['b7f459dg']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171151-b7f459dg/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171151-b7f459dg/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.374523870646954\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.20671245656334436\n",
            "1\n",
            "The running loss is:\n",
            "4.875691753812134\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.18752660591585132\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171151-b7f459dg/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171151-b7f459dg/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.123469600221142\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.27397960000850546\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_11PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_11PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171151-b7f459dg/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171151-b7f459dg/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171151-b7f459dg/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  697.734924\n",
            "66  2020-04-22  sub_region  ...             66  699.246887\n",
            "67  2020-04-23  sub_region  ...             67  698.447754\n",
            "68  2020-04-24  sub_region  ...             68  697.592102\n",
            "69  2020-04-25  sub_region  ...             69  697.349731\n",
            "70  2020-04-26  sub_region  ...             70  697.215332\n",
            "71  2020-04-27  sub_region  ...             71  698.537048\n",
            "72  2020-04-28  sub_region  ...             72  697.177002\n",
            "73  2020-04-29  sub_region  ...             73  699.459961\n",
            "74  2020-04-30  sub_region  ...             74  698.878723\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171151-b7f459dg/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171151-b7f459dg/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: b7f459dg \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171151-b7f459dg/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171151-b7f459dg/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171151-b7f459dg/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171151-b7f459dg/media/plotly/test_plot_20_70b23860.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171151-b7f459dg/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171151-b7f459dg/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171151-b7f459dg/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171151-b7f459dg/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: b7f459dg\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: de4cq8v7 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: de4cq8v7\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/de4cq8v7\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/de4cq8v7</a><br/>\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/de4cq8v7\n",
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          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
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        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171208-de4cq8v7/config.yaml\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "24.827137678861618\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.9548899107254468\n",
            "The running loss is:\n",
            "19.719864428043365\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.758456324155514\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171208-de4cq8v7/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171208-de4cq8v7/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.63474379479885\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.7167209151845712\n",
            "The running loss is:\n",
            "13.806637480854988\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.5310245184944227\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171208-de4cq8v7/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171208-de4cq8v7/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.331828974187374\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.4358395759302836\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171208-de4cq8v7/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171208-de4cq8v7/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.775343157351017\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.4144362752827314\n",
            "1\n",
            "The running loss is:\n",
            "10.32153557986021\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.3969821376869312\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['de4cq8v7']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171208-de4cq8v7/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171208-de4cq8v7/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.037654765415937\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.2706790294390745\n",
            "The running loss is:\n",
            "14.718151992186904\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.5660827689302655\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171208-de4cq8v7/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171208-de4cq8v7/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.737017912324518\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.22065453508940452\n",
            "Data saved to: \n",
            "28_May_202005_12PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_12PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171208-de4cq8v7/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171208-de4cq8v7/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171208-de4cq8v7/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  632.474121\n",
            "66  2020-04-22  sub_region  ...             66  634.329102\n",
            "67  2020-04-23  sub_region  ...             67  632.773071\n",
            "68  2020-04-24  sub_region  ...             68  631.713074\n",
            "69  2020-04-25  sub_region  ...             69  631.196228\n",
            "70  2020-04-26  sub_region  ...             70  631.151917\n",
            "71  2020-04-27  sub_region  ...             71  632.160889\n",
            "72  2020-04-28  sub_region  ...             72  631.541016\n",
            "73  2020-04-29  sub_region  ...             73  633.977478\n",
            "74  2020-04-30  sub_region  ...             74  632.605713\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171208-de4cq8v7/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171208-de4cq8v7/wandb-summary.json\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171208-de4cq8v7/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171208-de4cq8v7/wandb-history.jsonl\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171208-de4cq8v7/media/plotly/test_plot_20_a4b0535b.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171208-de4cq8v7/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171208-de4cq8v7/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: de4cq8v7 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171208-de4cq8v7/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171208-de4cq8v7/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171208-de4cq8v7/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171208-de4cq8v7/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171208-de4cq8v7/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171208-de4cq8v7/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: de4cq8v7\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: rm3rj91b with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: rm3rj91b\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/rm3rj91b\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/rm3rj91b</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/rm3rj91b\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnJtM3JqOTFiOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171226-rm3rj91b/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171226-rm3rj91b/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171226-rm3rj91b/media/graph/graph_0_summary_afe148b1.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171226-rm3rj91b/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171226-rm3rj91b/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171226-rm3rj91b/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171226-rm3rj91b/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171226-rm3rj91b/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171226-rm3rj91b/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.056726850569248\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 0\n",
            "0.5734545303774732\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171226-rm3rj91b/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171226-rm3rj91b/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "46.208357490599155\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 1\n",
            "1.6502984818071127\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171226-rm3rj91b/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171226-rm3rj91b/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "29.032308392226696\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 2\n",
            "1.0368681568652391\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171226-rm3rj91b/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171226-rm3rj91b/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "28.841471418738365\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 3\n",
            "1.0300525506692273\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['rm3rj91b']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171226-rm3rj91b/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171226-rm3rj91b/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "28.338371301069856\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 4\n",
            "1.0120846893239235\n",
            "1\n",
            "The running loss is:\n",
            "28.69572694809176\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 5\n",
            "1.0248473910032772\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171226-rm3rj91b/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171226-rm3rj91b/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.646056853234768\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 6\n",
            "0.7016448876155275\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_12PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171226-rm3rj91b/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171226-rm3rj91b/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_12PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  429.154022\n",
            "66  2020-04-22  sub_region  ...             66  428.729492\n",
            "67  2020-04-23  sub_region  ...             67  429.150635\n",
            "68  2020-04-24  sub_region  ...             68  429.542450\n",
            "69  2020-04-25  sub_region  ...             69  429.516174\n",
            "70  2020-04-26  sub_region  ...             70  429.553772\n",
            "71  2020-04-27  sub_region  ...             71  428.790649\n",
            "72  2020-04-28  sub_region  ...             72  429.774963\n",
            "73  2020-04-29  sub_region  ...             73  428.176636\n",
            "74  2020-04-30  sub_region  ...             74  428.942596\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171226-rm3rj91b/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171226-rm3rj91b/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171226-rm3rj91b/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171226-rm3rj91b/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171226-rm3rj91b/media/plotly/test_plot_14_bdfadf79.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171226-rm3rj91b/media\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171226-rm3rj91b/media/plotly\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: rm3rj91b \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171226-rm3rj91b/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171226-rm3rj91b/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171226-rm3rj91b/wandb-summary.json\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171226-rm3rj91b/media/plotly\n",
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            "INFO:wandb.wandb_agent:Cleaning up finished run: rm3rj91b\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
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          "output_type": "stream",
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            "wandb: Agent Starting Run: 6pb0wrjo with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 6pb0wrjo\n"
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          "name": "stdout"
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          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/6pb0wrjo\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/6pb0wrjo</a><br/>\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/6pb0wrjo\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
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            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
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          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171244-6pb0wrjo/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171244-6pb0wrjo/media/graph/graph_0_summary_5cc08279.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171244-6pb0wrjo/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171244-6pb0wrjo/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171244-6pb0wrjo/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171244-6pb0wrjo/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171244-6pb0wrjo/requirements.txt\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171244-6pb0wrjo/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.879746047779918\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 0\n",
            "0.4957052159921399\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171244-6pb0wrjo/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171244-6pb0wrjo/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "50.16468261182308\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 1\n",
            "1.7915958075651102\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171244-6pb0wrjo/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171244-6pb0wrjo/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "28.752860818058252\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 2\n",
            "1.0268878863592232\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171244-6pb0wrjo/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171244-6pb0wrjo/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "28.75844579562545\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 3\n",
            "1.027087349843766\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['6pb0wrjo']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171244-6pb0wrjo/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171244-6pb0wrjo/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "28.228818399831653\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 4\n",
            "1.0081720857082732\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171244-6pb0wrjo/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171244-6pb0wrjo/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "28.275303000351414\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 5\n",
            "1.0098322500125505\n",
            "2\n",
            "The running loss is:\n",
            "19.328091056086123\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 6\n",
            "0.6902889662887901\n",
            "3\n",
            "Stopping model now\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171244-6pb0wrjo/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171244-6pb0wrjo/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_12PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_12PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171244-6pb0wrjo/config.yaml\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  429.017181\n",
            "66  2020-04-22  sub_region  ...             66  428.986389\n",
            "67  2020-04-23  sub_region  ...             67  429.089111\n",
            "68  2020-04-24  sub_region  ...             68  429.224731\n",
            "69  2020-04-25  sub_region  ...             69  429.164246\n",
            "70  2020-04-26  sub_region  ...             70  429.191895\n",
            "71  2020-04-27  sub_region  ...             71  428.905151\n",
            "72  2020-04-28  sub_region  ...             72  429.181915\n",
            "73  2020-04-29  sub_region  ...             73  428.645477\n",
            "74  2020-04-30  sub_region  ...             74  428.955353\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171244-6pb0wrjo/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171244-6pb0wrjo/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171244-6pb0wrjo/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171244-6pb0wrjo/media/plotly/test_plot_14_562144b7.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171244-6pb0wrjo/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171244-6pb0wrjo/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 6pb0wrjo \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171244-6pb0wrjo/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171244-6pb0wrjo/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171244-6pb0wrjo/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171244-6pb0wrjo/media/plotly/test_plot_all_15_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171244-6pb0wrjo/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171244-6pb0wrjo/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 6pb0wrjo\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: o25lms00 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: o25lms00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/o25lms00\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/o25lms00</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/o25lms00\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/wandb-metadata.json\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOm8yNWxtczAwOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/media/graph/graph_0_summary_feea31d7.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.515796359628439\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.3524369022084607\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "31.958917625248432\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.1836636157499418\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.400996170938015\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.6444813396643709\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['o25lms00']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.490230657160282\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.647786320635566\n",
            "The running loss is:\n",
            "17.88597560673952\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.6624435409903526\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.275350011885166\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.6398277782179691\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.80729231238365\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.6595293449030982\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.339848961681128\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.6422166282104121\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.749763071537018\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.6203615952421118\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.160026656463742\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.6355565428319905\n",
            "Data saved to: \n",
            "28_May_202005_13PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_13PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  452.964355\n",
            "66  2020-04-22  sub_region  ...             66  448.373505\n",
            "67  2020-04-23  sub_region  ...             67  451.731903\n",
            "68  2020-04-24  sub_region  ...             68  454.205078\n",
            "69  2020-04-25  sub_region  ...             69  454.852173\n",
            "70  2020-04-26  sub_region  ...             70  454.945068\n",
            "71  2020-04-27  sub_region  ...             71  451.557831\n",
            "72  2020-04-28  sub_region  ...             72  453.470245\n",
            "73  2020-04-29  sub_region  ...             73  448.514465\n",
            "74  2020-04-30  sub_region  ...             74  451.503876\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/media/plotly/test_plot_20_da2b038c.plotly.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: o25lms00 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171259-o25lms00/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: o25lms00\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: de1bsktw with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: de1bsktw\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/de1bsktw\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/de1bsktw</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/de1bsktw\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmRlMWJza3R3OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/media/graph/graph_0_summary_f9cc4e35.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/requirements.txt\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.540501032024622\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.353351890074986\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "32.01947431452572\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.1859064560935453\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.433912321925163\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.6457004563675987\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.372653737664223\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.6434316199134897\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['de1bsktw']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.937954049557447\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.6643686685021277\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.54516338557005\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.649820866132224\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.718922704458237\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.6562563964614162\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.30660080164671\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.6409852148758041\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.718926943838596\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.6192195164384665\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.827449632808566\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.6232388752892062\n",
            "Data saved to: \n",
            "28_May_202005_13PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_13PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  457.992615\n",
            "66  2020-04-22  sub_region  ...             66  454.040710\n",
            "67  2020-04-23  sub_region  ...             67  456.812286\n",
            "68  2020-04-24  sub_region  ...             68  459.140167\n",
            "69  2020-04-25  sub_region  ...             69  459.434723\n",
            "70  2020-04-26  sub_region  ...             70  459.551544\n",
            "71  2020-04-27  sub_region  ...             71  456.045074\n",
            "72  2020-04-28  sub_region  ...             72  458.655548\n",
            "73  2020-04-29  sub_region  ...             73  453.452576\n",
            "74  2020-04-30  sub_region  ...             74  456.783661\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: de1bsktw \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/media/plotly/test_plot_20_c566c54f.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171320-de1bsktw/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: de1bsktw\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: hb7wkf8z with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: hb7wkf8z\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/hb7wkf8z\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/hb7wkf8z</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/hb7wkf8z\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmhiN3drZjh6OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/media/graph/graph_0_summary_0b1d345c.graph.json\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "31.62208504229784\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "1.17118833489992\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.408665716648102\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.7188394709869668\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.407186299562454\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7558217147986094\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.506580412387848\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.759502978236587\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['hb7wkf8z']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.30820331722498\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.7521556784157399\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.309663198888302\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.7151727110699371\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.341759957373142\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.679324442865672\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.737579837441444\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.6569474013867201\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.28192499279976\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.7141453701036947\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.758581429719925\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.694762275174812\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_13PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_13PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  337.341278\n",
            "66  2020-04-22  sub_region  ...             66  337.345581\n",
            "67  2020-04-23  sub_region  ...             67  337.355377\n",
            "68  2020-04-24  sub_region  ...             68  337.360901\n",
            "69  2020-04-25  sub_region  ...             69  337.360565\n",
            "70  2020-04-26  sub_region  ...             70  337.355988\n",
            "71  2020-04-27  sub_region  ...             71  337.354065\n",
            "72  2020-04-28  sub_region  ...             72  337.343140\n",
            "73  2020-04-29  sub_region  ...             73  337.349548\n",
            "74  2020-04-30  sub_region  ...             74  337.355652\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/media/plotly/test_plot_20_e91edfe1.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/media\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: hb7wkf8z \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171341-hb7wkf8z/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: hb7wkf8z\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: giily2eq with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: giily2eq\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/giily2eq\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/giily2eq</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/giily2eq\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmdpaWx5MmVxOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/media/graph/graph_0_summary_ae0c622c.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "31.686953715980053\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "1.1735908783696316\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.326208144426346\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.7157854868306054\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.44809077680111\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7573366954370782\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.70396327972412\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.7668134548045971\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['giily2eq']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.114036343991756\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.7449643090367317\n",
            "1\n",
            "The running loss is:\n",
            "19.89861784130335\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.7369858459741981\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.967432893812656\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.7024975145856539\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.17745415121317\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.7102760796745619\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.25604397803545\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.713186814001313\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.80481818318367\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.6964747475253211\n",
            "Data saved to: \n",
            "28_May_202005_14PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_14PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  353.958618\n",
            "66  2020-04-22  sub_region  ...             66  353.994934\n",
            "67  2020-04-23  sub_region  ...             67  353.966064\n",
            "68  2020-04-24  sub_region  ...             68  353.948608\n",
            "69  2020-04-25  sub_region  ...             69  353.950348\n",
            "70  2020-04-26  sub_region  ...             70  353.951172\n",
            "71  2020-04-27  sub_region  ...             71  353.985535\n",
            "72  2020-04-28  sub_region  ...             72  353.945618\n",
            "73  2020-04-29  sub_region  ...             73  354.007202\n",
            "74  2020-04-30  sub_region  ...             74  353.972107\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/media/plotly/test_plot_20_384740d4.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: giily2eq \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171401-giily2eq/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: giily2eq\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 27ymc3gp with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 27ymc3gp\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/27ymc3gp\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/27ymc3gp</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/27ymc3gp\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjI3eW1jM2dwOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/media/graph/graph_0_summary_32804e1d.graph.json\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "28.677166178822517\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "1.1029679299547122\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.185990750789642\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.737922721184217\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.213715583086014\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.777450599349462\n",
            "The running loss is:\n",
            "15.579263865947723\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.5992024563826047\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['27ymc3gp']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.614615187048912\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.6005621225788043\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.192182525992393\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.5458531740766305\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.893233135342598\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.6882012744362538\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.887526050209999\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.6110586942388461\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.056910991668701\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.50218888429495\n",
            "1\n",
            "The running loss is:\n",
            "10.211280450224876\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.39274155577787984\n",
            "2\n",
            "Data saved to: \n",
            "28_May_202005_14PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_14PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  409.355835\n",
            "66  2020-04-22  sub_region  ...             66  409.509674\n",
            "67  2020-04-23  sub_region  ...             67  409.471558\n",
            "68  2020-04-24  sub_region  ...             68  409.399261\n",
            "69  2020-04-25  sub_region  ...             69  409.354004\n",
            "70  2020-04-26  sub_region  ...             70  409.365173\n",
            "71  2020-04-27  sub_region  ...             71  409.424011\n",
            "72  2020-04-28  sub_region  ...             72  409.376404\n",
            "73  2020-04-29  sub_region  ...             73  409.552612\n",
            "74  2020-04-30  sub_region  ...             74  409.473022\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/media/plotly/test_plot_20_eefb9a0f.plotly.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 27ymc3gp \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171420-27ymc3gp/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 27ymc3gp\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 5fwqi39m with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 5fwqi39m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/5fwqi39m\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/5fwqi39m</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/5fwqi39m\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjVmd3FpMzltOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/media/graph/graph_0_summary_89a8d80e.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "28.61015349626541\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "1.1003905190871313\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.129196166992188\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.7357383141150842\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.223093792796135\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.7778112997229283\n",
            "The running loss is:\n",
            "15.592137679457664\n",
            "The number of items in train is: \n",
            "26\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The loss for epoch 3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0.599697603056064\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['5fwqi39m']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.741975575685501\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.5669990606032885\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.141681730747223\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.5439108357979701\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.65192748606205\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.6789202879254634\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.934842199087143\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.5359554691956594\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.86246071755886\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.4177869506753408\n",
            "The running loss is:\n",
            "9.102634258568287\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.3501013176372418\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_14PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_14PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  475.105896\n",
            "66  2020-04-22  sub_region  ...             66  475.061249\n",
            "67  2020-04-23  sub_region  ...             67  475.085632\n",
            "68  2020-04-24  sub_region  ...             68  475.112213\n",
            "69  2020-04-25  sub_region  ...             69  475.114807\n",
            "70  2020-04-26  sub_region  ...             70  475.139618\n",
            "71  2020-04-27  sub_region  ...             71  475.079346\n",
            "72  2020-04-28  sub_region  ...             72  475.173126\n",
            "73  2020-04-29  sub_region  ...             73  475.043427\n",
            "74  2020-04-30  sub_region  ...             74  475.085358\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/media/plotly/test_plot_20_114ca43c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/media\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 5fwqi39m \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171441-5fwqi39m/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 5fwqi39m\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 2d2cmon8 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 2d2cmon8\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/2d2cmon8\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/2d2cmon8</a><br/>\n",
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              "<IPython.core.display.HTML object>"
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/2d2cmon8\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjJkMmNtb244OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/media/graph/graph_0_summary_8a875c68.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "26.287698671221733\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "1.0110653335085282\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.019511476159096\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.8469042875445806\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.7527959048748\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.7597229194182616\n",
            "The running loss is:\n",
            "15.987582370638847\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.6149070142553403\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "15.31729331612587\n",
            "The number of items in train is: \n",
            "26\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['2d2cmon8']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The loss for epoch 4\n",
            "0.5891266660048411\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.650041669607162\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.5250016026771985\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.348268389701843\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.5133949380654556\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.254892647266388\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.4328804864333226\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_15PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_15PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  347.929901\n",
            "66  2020-04-22  sub_region  ...             66  348.376862\n",
            "67  2020-04-23  sub_region  ...             67  348.171692\n",
            "68  2020-04-24  sub_region  ...             68  347.880554\n",
            "69  2020-04-25  sub_region  ...             69  347.761993\n",
            "70  2020-04-26  sub_region  ...             70  347.774658\n",
            "71  2020-04-27  sub_region  ...             71  348.046265\n",
            "72  2020-04-28  sub_region  ...             72  347.813934\n",
            "73  2020-04-29  sub_region  ...             73  348.664154\n",
            "74  2020-04-30  sub_region  ...             74  348.247253\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/wandb-summary.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/media/plotly/test_plot_16_3f181331.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 2d2cmon8 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/media/plotly/test_plot_all_17_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171503-2d2cmon8/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 2d2cmon8\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: qlrtjgmw with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: qlrtjgmw\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/qlrtjgmw\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/qlrtjgmw</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/qlrtjgmw\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnFscnRqZ213OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/media/graph/graph_0_summary_c8fe355a.graph.json\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "26.332314282655716\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "1.0127813185636814\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.822422578930855\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.8393239453434944\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.969776809215546\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.7680683388159826\n",
            "The running loss is:\n",
            "15.921830900013447\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.6123781115389787\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "15.494909837841988\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.5959580706862303\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['qlrtjgmw']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.208502128720284\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.5464808511046263\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.151983082294464\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.621230118549787\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.41115165501833\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.4773519867314742\n",
            "The running loss is:\n",
            "9.447976488620043\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.3633837111007709\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.989659074693918\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.3842176567189969\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_15PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_15PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  578.508301\n",
            "66  2020-04-22  sub_region  ...             66  578.518188\n",
            "67  2020-04-23  sub_region  ...             67  578.516479\n",
            "68  2020-04-24  sub_region  ...             68  578.510986\n",
            "69  2020-04-25  sub_region  ...             69  578.503906\n",
            "70  2020-04-26  sub_region  ...             70  578.503906\n",
            "71  2020-04-27  sub_region  ...             71  578.500427\n",
            "72  2020-04-28  sub_region  ...             72  578.506470\n",
            "73  2020-04-29  sub_region  ...             73  578.516052\n",
            "74  2020-04-30  sub_region  ...             74  578.512817\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/wandb-summary.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/media/plotly/test_plot_20_ae1f0e4f.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/media/plotly\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: qlrtjgmw \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171519-qlrtjgmw/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: qlrtjgmw\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: t3gzqxhh with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: t3gzqxhh\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/t3gzqxhh\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/t3gzqxhh</a><br/>\n",
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          "metadata": {
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        {
          "output_type": "stream",
          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/t3gzqxhh\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnQzZ3pxeGhoOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "15.545928395004012\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 0\n",
            "0.5552117283930004\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171535-t3gzqxhh/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171535-t3gzqxhh/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171535-t3gzqxhh/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171535-t3gzqxhh/media/graph/graph_0_summary_187e9097.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171535-t3gzqxhh/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171535-t3gzqxhh/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171535-t3gzqxhh/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171535-t3gzqxhh/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171535-t3gzqxhh/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.661523299873807\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 1\n",
            "0.7736258321383502\n",
            "The running loss is:\n",
            "14.07579830638133\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 2\n",
            "0.5027070823707618\n",
            "The running loss is:\n",
            "11.572435699170455\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171535-t3gzqxhh/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171535-t3gzqxhh/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 3\n",
            "0.4133012749703734\n",
            "1\n",
            "The running loss is:\n",
            "11.783770642941818\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 4\n",
            "0.42084895153363633\n",
            "The running loss is:\n",
            "9.61011728970334\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 5\n",
            "0.3432184746322621\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171535-t3gzqxhh/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171535-t3gzqxhh/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.171849727863446\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 6\n",
            "0.4704232045665516\n",
            "The running loss is:\n",
            "9.562567624612711\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 7\n",
            "0.34152027230759685\n",
            "1\n",
            "The running loss is:\n",
            "11.004484111152124\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 8\n",
            "0.3930172896840044\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171535-t3gzqxhh/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171535-t3gzqxhh/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.858391232584836\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 9\n",
            "0.38779968687802985\n",
            "Data saved to: \n",
            "28_May_202005_15PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_15PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['t3gzqxhh']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171535-t3gzqxhh/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171535-t3gzqxhh/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171535-t3gzqxhh/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  856.106934\n",
            "66  2020-04-22  sub_region  ...             66  898.349731\n",
            "67  2020-04-23  sub_region  ...             67  857.475891\n",
            "68  2020-04-24  sub_region  ...             68  861.468872\n",
            "69  2020-04-25  sub_region  ...             69  885.428162\n",
            "70  2020-04-26  sub_region  ...             70  897.812500\n",
            "71  2020-04-27  sub_region  ...             71  894.293274\n",
            "72  2020-04-28  sub_region  ...             72  929.067383\n",
            "73  2020-04-29  sub_region  ...             73  878.263550\n",
            "74  2020-04-30  sub_region  ...             74  869.549072\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171535-t3gzqxhh/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171535-t3gzqxhh/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171535-t3gzqxhh/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171535-t3gzqxhh/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171535-t3gzqxhh/media/plotly/test_plot_20_314de4b9.plotly.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171535-t3gzqxhh/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171535-t3gzqxhh/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: t3gzqxhh \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171535-t3gzqxhh/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171535-t3gzqxhh/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171535-t3gzqxhh/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171535-t3gzqxhh/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171535-t3gzqxhh/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171535-t3gzqxhh/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: t3gzqxhh\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: qludt1wz with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: qludt1wz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/qludt1wz\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/qludt1wz</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/qludt1wz\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnFsdWR0MXd6OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "15.36638443195261\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 0\n",
            "0.5487994439983075\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171546-qludt1wz/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171546-qludt1wz/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171546-qludt1wz/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171546-qludt1wz/wandb-events.jsonl\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171546-qludt1wz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171546-qludt1wz/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171546-qludt1wz/media/graph\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.380546062253416\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 1\n",
            "0.6921623593661934\n",
            "The running loss is:\n",
            "13.36081693135202\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 2\n",
            "0.4771720332625721\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171546-qludt1wz/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171546-qludt1wz/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.953934513032436\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 3\n",
            "0.4269262326083013\n",
            "1\n",
            "The running loss is:\n",
            "12.163511937658768\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 4\n",
            "0.43441114063067027\n",
            "The running loss is:\n",
            "9.378338906506542\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 5\n",
            "0.33494067523237653\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171546-qludt1wz/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171546-qludt1wz/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.704894061491359\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 6\n",
            "0.4537462164818343\n",
            "The running loss is:\n",
            "11.437989893951453\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 7\n",
            "0.4084996390696948\n",
            "1\n",
            "The running loss is:\n",
            "11.367679933318868\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 8\n",
            "0.40598856904710245\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171546-qludt1wz/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171546-qludt1wz/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.42585400538519\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 9\n",
            "0.40806621447804253\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_15PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_15PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['qludt1wz']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171546-qludt1wz/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171546-qludt1wz/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171546-qludt1wz/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  802.753906\n",
            "66  2020-04-22  sub_region  ...             66  877.084839\n",
            "67  2020-04-23  sub_region  ...             67  830.681274\n",
            "68  2020-04-24  sub_region  ...             68  816.030273\n",
            "69  2020-04-25  sub_region  ...             69  821.921509\n",
            "70  2020-04-26  sub_region  ...             70  826.182373\n",
            "71  2020-04-27  sub_region  ...             71  846.830322\n",
            "72  2020-04-28  sub_region  ...             72  856.710266\n",
            "73  2020-04-29  sub_region  ...             73  857.412720\n",
            "74  2020-04-30  sub_region  ...             74  841.012085\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171546-qludt1wz/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171546-qludt1wz/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171546-qludt1wz/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171546-qludt1wz/wandb-summary.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171546-qludt1wz/media/plotly/test_plot_20_e30096eb.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171546-qludt1wz/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171546-qludt1wz/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: qludt1wz \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171546-qludt1wz/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171546-qludt1wz/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171546-qludt1wz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171546-qludt1wz/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171546-qludt1wz/media/plotly\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171546-qludt1wz/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: qludt1wz\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: i3u83f7j with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: i3u83f7j\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/i3u83f7j\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/i3u83f7j</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/i3u83f7j\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmkzdTgzZjdqOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "10.473457891494036\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.38790584783311244\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171601-i3u83f7j/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171601-i3u83f7j/media/graph/graph_0_summary_c269f7d4.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171601-i3u83f7j/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171601-i3u83f7j/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171601-i3u83f7j/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171601-i3u83f7j/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171601-i3u83f7j/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171601-i3u83f7j/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171601-i3u83f7j/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.50141921825707\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.4630155266021137\n",
            "1\n",
            "The running loss is:\n",
            "12.783501834142953\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.47346303089418346\n",
            "The running loss is:\n",
            "5.422599470242858\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.20083701741640214\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171601-i3u83f7j/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171601-i3u83f7j/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "5.399664411786944\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.19998757080692384\n",
            "The running loss is:\n",
            "4.413807349279523\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.16347434626961196\n",
            "The running loss is:\n",
            "4.564219214487821\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.16904515609214152\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171601-i3u83f7j/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171601-i3u83f7j/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.552914118394256\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.16862644882941688\n",
            "1\n",
            "The running loss is:\n",
            "4.2418790506199\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.15710663150444074\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171601-i3u83f7j/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171601-i3u83f7j/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.331287162844092\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.16041804306829968\n",
            "Data saved to: \n",
            "28_May_202005_16PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_16PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['i3u83f7j']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  839.469238\n",
            "66  2020-04-22  sub_region  ...             66  751.845520\n",
            "67  2020-04-23  sub_region  ...             67  796.254761\n",
            "68  2020-04-24  sub_region  ...             68  840.047058\n",
            "69  2020-04-25  sub_region  ...             69  856.757812\n",
            "70  2020-04-26  sub_region  ...             70  857.516724\n",
            "71  2020-04-27  sub_region  ...             71  805.222778\n",
            "72  2020-04-28  sub_region  ...             72  846.005188\n",
            "73  2020-04-29  sub_region  ...             73  746.322815\n",
            "74  2020-04-30  sub_region  ...             74  799.226501\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171601-i3u83f7j/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171601-i3u83f7j/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171601-i3u83f7j/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171601-i3u83f7j/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171601-i3u83f7j/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171601-i3u83f7j/media/plotly/test_plot_20_908a465b.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171601-i3u83f7j/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171601-i3u83f7j/media/plotly\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: i3u83f7j \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171601-i3u83f7j/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171601-i3u83f7j/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171601-i3u83f7j/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171601-i3u83f7j/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171601-i3u83f7j/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171601-i3u83f7j/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: i3u83f7j\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: ud7enlgo with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: ud7enlgo\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/ud7enlgo\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/ud7enlgo</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/ud7enlgo\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnVkN2VubGdvOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "10.56319503299892\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.39122944566662665\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171612-ud7enlgo/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171612-ud7enlgo/media/graph/graph_0_summary_fb5a18e4.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171612-ud7enlgo/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171612-ud7enlgo/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171612-ud7enlgo/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171612-ud7enlgo/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171612-ud7enlgo/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171612-ud7enlgo/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171612-ud7enlgo/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.365171766839921\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.45796932469777485\n",
            "1\n",
            "The running loss is:\n",
            "10.063402196858078\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.37271859988363254\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171612-ud7enlgo/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171612-ud7enlgo/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.191733076237142\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.19228641023100526\n",
            "1\n",
            "The running loss is:\n",
            "5.048446481116116\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.1869794993005969\n",
            "The running loss is:\n",
            "4.6334264650940895\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.17160838759607738\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171612-ud7enlgo/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171612-ud7enlgo/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.809368921909481\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.17812477488553635\n",
            "The running loss is:\n",
            "4.469904407858849\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.16555201510588327\n",
            "1\n",
            "The running loss is:\n",
            "4.4312631585635245\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.164120857724575\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171612-ud7enlgo/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171612-ud7enlgo/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.365964470198378\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.1617023877851251\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_16PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_16PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['ud7enlgo']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171612-ud7enlgo/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171612-ud7enlgo/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171612-ud7enlgo/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  820.661011\n",
            "66  2020-04-22  sub_region  ...             66  731.493591\n",
            "67  2020-04-23  sub_region  ...             67  777.978394\n",
            "68  2020-04-24  sub_region  ...             68  817.230530\n",
            "69  2020-04-25  sub_region  ...             69  833.734131\n",
            "70  2020-04-26  sub_region  ...             70  830.960205\n",
            "71  2020-04-27  sub_region  ...             71  781.981934\n",
            "72  2020-04-28  sub_region  ...             72  821.894836\n",
            "73  2020-04-29  sub_region  ...             73  732.720215\n",
            "74  2020-04-30  sub_region  ...             74  782.010132\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171612-ud7enlgo/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171612-ud7enlgo/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171612-ud7enlgo/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171612-ud7enlgo/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171612-ud7enlgo/media/plotly/test_plot_20_a6760719.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171612-ud7enlgo/media\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171612-ud7enlgo/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: ud7enlgo \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171612-ud7enlgo/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171612-ud7enlgo/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171612-ud7enlgo/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171612-ud7enlgo/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171612-ud7enlgo/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171612-ud7enlgo/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: ud7enlgo\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: wwxi05mq with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: wwxi05mq\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/wwxi05mq\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/wwxi05mq</a><br/>\n",
              "            "
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/wwxi05mq\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnd3eGkwNW1xOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "22.41447990387678\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.8301659223658068\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171622-wwxi05mq/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171622-wwxi05mq/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171622-wwxi05mq/media/graph/graph_0_summary_486438ab.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171622-wwxi05mq/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171622-wwxi05mq/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171622-wwxi05mq/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171622-wwxi05mq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171622-wwxi05mq/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171622-wwxi05mq/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.35543255507946\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.7539049094473874\n",
            "1\n",
            "The running loss is:\n",
            "19.27651271224022\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7139449152681563\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171622-wwxi05mq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171622-wwxi05mq/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "12.351722501218319\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.4574712037488266\n",
            "1\n",
            "The running loss is:\n",
            "9.813126027584076\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.36344911213274356\n",
            "The running loss is:\n",
            "6.200845863670111\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.2296609579137078\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171622-wwxi05mq/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171622-wwxi05mq/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.99026171118021\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.2218615448585263\n",
            "The running loss is:\n",
            "5.51112406142056\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.20411570597853926\n",
            "1\n",
            "The running loss is:\n",
            "5.962472440674901\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.22083231261758893\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171622-wwxi05mq/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171622-wwxi05mq/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.207641955465078\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.1928756279801881\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_16PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.wandb_agent:Running runs: ['wwxi05mq']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_16PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171622-wwxi05mq/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171622-wwxi05mq/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171622-wwxi05mq/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  731.740784\n",
            "66  2020-04-22  sub_region  ...             66  738.606201\n",
            "67  2020-04-23  sub_region  ...             67  737.777405\n",
            "68  2020-04-24  sub_region  ...             68  726.210022\n",
            "69  2020-04-25  sub_region  ...             69  719.908691\n",
            "70  2020-04-26  sub_region  ...             70  719.150635\n",
            "71  2020-04-27  sub_region  ...             71  720.189941\n",
            "72  2020-04-28  sub_region  ...             72  731.834473\n",
            "73  2020-04-29  sub_region  ...             73  735.288940\n",
            "74  2020-04-30  sub_region  ...             74  731.156189\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171622-wwxi05mq/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171622-wwxi05mq/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171622-wwxi05mq/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171622-wwxi05mq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171622-wwxi05mq/media/plotly/test_plot_20_3857be08.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171622-wwxi05mq/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171622-wwxi05mq/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: wwxi05mq \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171622-wwxi05mq/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171622-wwxi05mq/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171622-wwxi05mq/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171622-wwxi05mq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171622-wwxi05mq/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171622-wwxi05mq/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: wwxi05mq\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: g7arybyl with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: g7arybyl\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/g7arybyl\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/g7arybyl</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/g7arybyl\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmc3YXJ5YnlsOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "22.289740599691868\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.8255459481367359\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171638-g7arybyl/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171638-g7arybyl/media/graph/graph_0_summary_fcaaf699.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171638-g7arybyl/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171638-g7arybyl/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171638-g7arybyl/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171638-g7arybyl/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171638-g7arybyl/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171638-g7arybyl/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171638-g7arybyl/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.721937209367752\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.7674791559025094\n",
            "1\n",
            "The running loss is:\n",
            "19.501514554023743\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7222783168156942\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171638-g7arybyl/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171638-g7arybyl/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.022847175598145\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.4823276731703017\n",
            "1\n",
            "The running loss is:\n",
            "9.215275160968304\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.3413064874432705\n",
            "2\n",
            "The running loss is:\n",
            "6.453644126653671\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.23902385654272856\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171638-g7arybyl/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171638-g7arybyl/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.3195782247930765\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.23405845277011395\n",
            "1\n",
            "The running loss is:\n",
            "5.538634759373963\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.20513462071755417\n",
            "The running loss is:\n",
            "6.0211376789957285\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.22300509922206402\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171638-g7arybyl/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171638-g7arybyl/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.4065384501591325\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.2002421648207086\n",
            "Data saved to: \n",
            "28_May_202005_16PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_16PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['g7arybyl']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171638-g7arybyl/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171638-g7arybyl/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([1, 10, 9])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171638-g7arybyl/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  723.085754\n",
            "66  2020-04-22  sub_region  ...             66  731.862305\n",
            "67  2020-04-23  sub_region  ...             67  732.741089\n",
            "68  2020-04-24  sub_region  ...             68  723.526733\n",
            "69  2020-04-25  sub_region  ...             69  715.763306\n",
            "70  2020-04-26  sub_region  ...             70  713.154541\n",
            "71  2020-04-27  sub_region  ...             71  712.077576\n",
            "72  2020-04-28  sub_region  ...             72  725.158813\n",
            "73  2020-04-29  sub_region  ...             73  729.568176\n",
            "74  2020-04-30  sub_region  ...             74  726.045166\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171638-g7arybyl/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171638-g7arybyl/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171638-g7arybyl/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171638-g7arybyl/media/plotly/test_plot_20_967a32da.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171638-g7arybyl/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171638-g7arybyl/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: g7arybyl \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171638-g7arybyl/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171638-g7arybyl/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171638-g7arybyl/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171638-g7arybyl/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
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            "INFO:wandb.wandb_agent:Cleaning up finished run: g7arybyl\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
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            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
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            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
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            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: e6thpjz5\n"
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          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/e6thpjz5\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/e6thpjz5</a><br/>\n",
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            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "17.69617347419262\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.6806220566997161\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171655-e6thpjz5/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171655-e6thpjz5/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171655-e6thpjz5/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171655-e6thpjz5/media/graph/graph_0_summary_4c0ec5ce.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171655-e6thpjz5/wandb-events.jsonl\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171655-e6thpjz5/wandb-metadata.json\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.942448504269123\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.4977864809334278\n",
            "1\n",
            "The running loss is:\n",
            "11.007499657571316\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.4233653714450506\n",
            "2\n",
            "The running loss is:\n",
            "7.731042757630348\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.297347798370398\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171655-e6thpjz5/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171655-e6thpjz5/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.677496463060379\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.1799037101177069\n",
            "1\n",
            "The running loss is:\n",
            "3.537908681668341\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.13607341083339775\n",
            "The running loss is:\n",
            "3.6070269970223308\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.13873180757778195\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171655-e6thpjz5/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171655-e6thpjz5/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.469477290287614\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.21036451116490823\n",
            "2\n",
            "The running loss is:\n",
            "4.373710255138576\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.16821962519763753\n",
            "The running loss is:\n",
            "4.385507521219552\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.168673366200752\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171655-e6thpjz5/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171655-e6thpjz5/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_16PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_16PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['e6thpjz5']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171655-e6thpjz5/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171655-e6thpjz5/wandb-summary.json\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  641.062012\n",
            "66  2020-04-22  sub_region  ...             66  649.469788\n",
            "67  2020-04-23  sub_region  ...             67  662.655884\n",
            "68  2020-04-24  sub_region  ...             68  663.000610\n",
            "69  2020-04-25  sub_region  ...             69  652.813110\n",
            "70  2020-04-26  sub_region  ...             70  647.735291\n",
            "71  2020-04-27  sub_region  ...             71  634.131409\n",
            "72  2020-04-28  sub_region  ...             72  641.305664\n",
            "73  2020-04-29  sub_region  ...             73  641.875549\n",
            "74  2020-04-30  sub_region  ...             74  656.309570\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171655-e6thpjz5/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171655-e6thpjz5/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171655-e6thpjz5/wandb-summary.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171655-e6thpjz5/media/plotly/test_plot_20_7320596b.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171655-e6thpjz5/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171655-e6thpjz5/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: e6thpjz5 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171655-e6thpjz5/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171655-e6thpjz5/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171655-e6thpjz5/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171655-e6thpjz5/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171655-e6thpjz5/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171655-e6thpjz5/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: e6thpjz5\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: awvxuc0v with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: awvxuc0v\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/awvxuc0v\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/awvxuc0v</a><br/>\n",
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          "metadata": {
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        {
          "output_type": "stream",
          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/awvxuc0v\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmF3dnh1YzB2OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "17.936274334788322\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.6898567051841662\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171710-awvxuc0v/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171710-awvxuc0v/media/graph/graph_0_summary_6048a156.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171710-awvxuc0v/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171710-awvxuc0v/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171710-awvxuc0v/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171710-awvxuc0v/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171710-awvxuc0v/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171710-awvxuc0v/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171710-awvxuc0v/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.563346169888973\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.48320562191880667\n",
            "1\n",
            "The running loss is:\n",
            "10.63600341975689\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.4090770546060342\n",
            "2\n",
            "The running loss is:\n",
            "7.032686905935407\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.27048795792059255\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171710-awvxuc0v/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171710-awvxuc0v/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.1291360929608345\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.1588129266523398\n",
            "1\n",
            "The running loss is:\n",
            "3.5215289955958724\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.13544342290753356\n",
            "The running loss is:\n",
            "4.55407845415175\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.17515686362122113\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171710-awvxuc0v/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171710-awvxuc0v/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "3.83646054379642\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.14755617476140076\n",
            "2\n",
            "The running loss is:\n",
            "4.684140918310732\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.18015926608887428\n",
            "The running loss is:\n",
            "7.284909620881081\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.28018883157234925\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171710-awvxuc0v/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171710-awvxuc0v/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "Data saved to: \n",
            "28_May_202005_17PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_17PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['awvxuc0v']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  811.324219\n",
            "66  2020-04-22  sub_region  ...             66  801.534241\n",
            "67  2020-04-23  sub_region  ...             67  837.117920\n",
            "68  2020-04-24  sub_region  ...             68  854.320984\n",
            "69  2020-04-25  sub_region  ...             69  834.952698\n",
            "70  2020-04-26  sub_region  ...             70  831.260376\n",
            "71  2020-04-27  sub_region  ...             71  782.874146\n",
            "72  2020-04-28  sub_region  ...             72  832.120972\n",
            "73  2020-04-29  sub_region  ...             73  785.114258\n",
            "74  2020-04-30  sub_region  ...             74  817.378418\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171710-awvxuc0v/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171710-awvxuc0v/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171710-awvxuc0v/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: awvxuc0v \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171710-awvxuc0v/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171710-awvxuc0v/media/plotly/test_plot_20_d2368e1b.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171710-awvxuc0v/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171710-awvxuc0v/media\n",
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            "INFO:wandb.wandb_agent:Cleaning up finished run: awvxuc0v\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
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          "text": [
            "wandb: Agent Starting Run: rjmkv5tz with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: rjmkv5tz\n"
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          "name": "stdout"
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          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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          "name": "stderr"
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        {
          "output_type": "display_data",
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/rjmkv5tz\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/rjmkv5tz</a><br/>\n",
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          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
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        {
          "output_type": "stream",
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "16.614094384014606\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.639003630154408\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171721-rjmkv5tz/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171721-rjmkv5tz/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171721-rjmkv5tz/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171721-rjmkv5tz/media/graph/graph_0_summary_5ed96a4e.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171721-rjmkv5tz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171721-rjmkv5tz/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171721-rjmkv5tz/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171721-rjmkv5tz/media/graph\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.784377932548523\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.49170684355955857\n",
            "1\n",
            "The running loss is:\n",
            "10.317914854735136\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.3968428790282745\n",
            "The running loss is:\n",
            "8.098226331174374\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.31147024350670666\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171721-rjmkv5tz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171721-rjmkv5tz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.721522863255814\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.22005857166368514\n",
            "The running loss is:\n",
            "4.689897304400802\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.180380665553877\n",
            "The running loss is:\n",
            "6.495792439207435\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.24983817073874748\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171721-rjmkv5tz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171721-rjmkv5tz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.418599771335721\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.20840768351291233\n",
            "The running loss is:\n",
            "4.195066409185529\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.16134870804559726\n",
            "1\n",
            "The running loss is:\n",
            "5.375888114795089\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.2067649274921188\n",
            "2\n",
            "Data saved to: \n",
            "28_May_202005_17PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171721-rjmkv5tz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171721-rjmkv5tz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_17PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['rjmkv5tz']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  730.216797\n",
            "66  2020-04-22  sub_region  ...             66  741.966797\n",
            "67  2020-04-23  sub_region  ...             67  738.005005\n",
            "68  2020-04-24  sub_region  ...             68  724.475220\n",
            "69  2020-04-25  sub_region  ...             69  719.175964\n",
            "70  2020-04-26  sub_region  ...             70  717.805908\n",
            "71  2020-04-27  sub_region  ...             71  714.661133\n",
            "72  2020-04-28  sub_region  ...             72  731.062378\n",
            "73  2020-04-29  sub_region  ...             73  725.214966\n",
            "74  2020-04-30  sub_region  ...             74  723.802612\n",
            "\n",
            "[20 rows x 28 columns]"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171721-rjmkv5tz/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
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          "name": "stdout"
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          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171721-rjmkv5tz/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171721-rjmkv5tz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171721-rjmkv5tz/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171721-rjmkv5tz/media/plotly/test_plot_20_54bf2aea.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171721-rjmkv5tz/media\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171721-rjmkv5tz/media/plotly\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: rjmkv5tz \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171721-rjmkv5tz/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171721-rjmkv5tz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171721-rjmkv5tz/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171721-rjmkv5tz/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171721-rjmkv5tz/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171721-rjmkv5tz/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: rjmkv5tz\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: xl6invys with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: xl6invys\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/xl6invys\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/xl6invys</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/xl6invys\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnhsNmludnlzOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "16.377284348011017\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.6298955518465775\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171732-xl6invys/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171732-xl6invys/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171732-xl6invys/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171732-xl6invys/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171732-xl6invys/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171732-xl6invys/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171732-xl6invys/media/graph/graph_0_summary_53b91896.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171732-xl6invys/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171732-xl6invys/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.166790530085564\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.5448765588494447\n",
            "The running loss is:\n",
            "9.428598899394274\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.3626384192074721\n",
            "1\n",
            "The running loss is:\n",
            "6.6330753564834595\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.2551182829416715\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171732-xl6invys/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171732-xl6invys/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.67338194232434\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.2182069977817054\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_17PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_17PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171732-xl6invys/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171732-xl6invys/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171732-xl6invys/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  339.937469\n",
            "66  2020-04-22  sub_region  ...             66  348.662567\n",
            "67  2020-04-23  sub_region  ...             67  321.414612\n",
            "68  2020-04-24  sub_region  ...             68  302.783997\n",
            "69  2020-04-25  sub_region  ...             69  324.099854\n",
            "70  2020-04-26  sub_region  ...             70  317.279297\n",
            "71  2020-04-27  sub_region  ...             71  371.108948\n",
            "72  2020-04-28  sub_region  ...             72  297.625122\n",
            "73  2020-04-29  sub_region  ...             73  384.647125\n",
            "74  2020-04-30  sub_region  ...             74  332.773010\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171732-xl6invys/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171732-xl6invys/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.wandb_agent:Running runs: ['xl6invys']\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171732-xl6invys/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171732-xl6invys/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171732-xl6invys/media/plotly/test_plot_10_807ad6e3.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171732-xl6invys/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171732-xl6invys/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: xl6invys \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171732-xl6invys/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171732-xl6invys/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171732-xl6invys/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171732-xl6invys/media/plotly/test_plot_all_11_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171732-xl6invys/media/plotly\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171732-xl6invys/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: xl6invys\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
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            "wandb: Agent Starting Run: iamu2dq6 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: iamu2dq6\n"
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          "name": "stdout"
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          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/iamu2dq6\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/iamu2dq6</a><br/>\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/iamu2dq6\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmlhbXUyZHE2OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
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          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171742-iamu2dq6/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171742-iamu2dq6/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171742-iamu2dq6/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171742-iamu2dq6/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171742-iamu2dq6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171742-iamu2dq6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171742-iamu2dq6/media/graph/graph_0_summary_a8e312dd.graph.json\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171742-iamu2dq6/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.114518700167537\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 0\n",
            "0.5755185250059834\n",
            "The running loss is:\n",
            "40.93923968449235\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 1\n",
            "1.462115703017584\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171742-iamu2dq6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171742-iamu2dq6/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.72650630073622\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 2\n",
            "0.6330895107405793\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171742-iamu2dq6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171742-iamu2dq6/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.82603452168405\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 3\n",
            "0.4937869472030018\n",
            "The running loss is:\n",
            "12.235369089990854\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 4\n",
            "0.43697746749967337\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171742-iamu2dq6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171742-iamu2dq6/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.42129836557433\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 5\n",
            "0.4436177987705118\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['iamu2dq6']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.593332045944408\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 6\n",
            "0.4140475730694431\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171742-iamu2dq6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171742-iamu2dq6/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.022986069088802\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 7\n",
            "0.42939235961031436\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171742-iamu2dq6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171742-iamu2dq6/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.050711158488411\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 8\n",
            "0.3946682556603004\n",
            "2\n",
            "The running loss is:\n",
            "10.95676115965398\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 9\n",
            "0.3913128985590707\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_17PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171742-iamu2dq6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171742-iamu2dq6/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_17PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  837.403687\n",
            "66  2020-04-22  sub_region  ...             66  830.898865\n",
            "67  2020-04-23  sub_region  ...             67  824.932678\n",
            "68  2020-04-24  sub_region  ...             68  836.498291\n",
            "69  2020-04-25  sub_region  ...             69  841.817627\n",
            "70  2020-04-26  sub_region  ...             70  846.289368\n",
            "71  2020-04-27  sub_region  ...             71  830.351929\n",
            "72  2020-04-28  sub_region  ...             72  852.223999\n",
            "73  2020-04-29  sub_region  ...             73  823.185059\n",
            "74  2020-04-30  sub_region  ...             74  832.774414\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171742-iamu2dq6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171742-iamu2dq6/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: iamu2dq6 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171742-iamu2dq6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171742-iamu2dq6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171742-iamu2dq6/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171742-iamu2dq6/media/plotly/test_plot_20_754d0a08.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171742-iamu2dq6/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171742-iamu2dq6/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171742-iamu2dq6/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171742-iamu2dq6/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: iamu2dq6\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: dkl5ngk8 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: dkl5ngk8\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/dkl5ngk8\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/dkl5ngk8</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/dkl5ngk8\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmRrbDVuZ2s4OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171758-dkl5ngk8/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171758-dkl5ngk8/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171758-dkl5ngk8/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171758-dkl5ngk8/media/graph/graph_0_summary_4935b97e.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171758-dkl5ngk8/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171758-dkl5ngk8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171758-dkl5ngk8/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171758-dkl5ngk8/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171758-dkl5ngk8/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.39689282909967\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 0\n",
            "0.5498890296107025\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171758-dkl5ngk8/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171758-dkl5ngk8/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "44.767726235091686\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 1\n",
            "1.5988473655389888\n",
            "The running loss is:\n",
            "22.245736518874764\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 2\n",
            "0.794490589959813\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171758-dkl5ngk8/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171758-dkl5ngk8/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.605747589841485\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 3\n",
            "0.5573481282086244\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171758-dkl5ngk8/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171758-dkl5ngk8/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.130321642383933\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 4\n",
            "0.6475114872279976\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_18PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.wandb_agent:Running runs: ['dkl5ngk8']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_18PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171758-dkl5ngk8/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171758-dkl5ngk8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171758-dkl5ngk8/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  410.513519\n",
            "66  2020-04-22  sub_region  ...             66  402.391907\n",
            "67  2020-04-23  sub_region  ...             67  409.347412\n",
            "68  2020-04-24  sub_region  ...             68  416.087708\n",
            "69  2020-04-25  sub_region  ...             69  416.266327\n",
            "70  2020-04-26  sub_region  ...             70  415.997223\n",
            "71  2020-04-27  sub_region  ...             71  405.508575\n",
            "72  2020-04-28  sub_region  ...             72  415.981689\n",
            "73  2020-04-29  sub_region  ...             73  393.018555\n",
            "74  2020-04-30  sub_region  ...             74  405.671448\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171758-dkl5ngk8/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171758-dkl5ngk8/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171758-dkl5ngk8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171758-dkl5ngk8/media/plotly/test_plot_10_d4fb56b9.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171758-dkl5ngk8/media\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171758-dkl5ngk8/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: dkl5ngk8 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171758-dkl5ngk8/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171758-dkl5ngk8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171758-dkl5ngk8/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171758-dkl5ngk8/media/plotly/test_plot_all_11_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171758-dkl5ngk8/media/plotly\n",
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            "INFO:wandb.wandb_agent:Cleaning up finished run: dkl5ngk8\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
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            "wandb: Agent Starting Run: op8okjem with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: op8okjem\n"
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          "name": "stdout"
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          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/op8okjem\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/op8okjem</a><br/>\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/op8okjem\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
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            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOm9wOG9ramVtOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
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          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
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        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/media/graph/graph_0_summary_ab59a122.graph.json\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.718430198729038\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.3969788962492236\n",
            "The running loss is:\n",
            "28.698574017733335\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.0629101488049384\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.018407717347145\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.5932743599017462\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.534781254827976\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.39017708351214725\n",
            "2\n",
            "The running loss is:\n",
            "5.385364955291152\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.1994579613070797\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.625352939590812\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.28242047924410413\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['op8okjem']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.7238668041536584\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.2490321038575429\n",
            "The running loss is:\n",
            "7.8772421116009355\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.29174970783707166\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.317102946341038\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.27100381282744584\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.420254968106747\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.20075018400395359\n",
            "Data saved to: \n",
            "28_May_202005_18PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_18PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/wandb-summary.json\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  779.043823\n",
            "66  2020-04-22  sub_region  ...             66  778.755005\n",
            "67  2020-04-23  sub_region  ...             67  778.881592\n",
            "68  2020-04-24  sub_region  ...             68  779.025024\n",
            "69  2020-04-25  sub_region  ...             69  779.112366\n",
            "70  2020-04-26  sub_region  ...             70  779.083008\n",
            "71  2020-04-27  sub_region  ...             71  778.987671\n",
            "72  2020-04-28  sub_region  ...             72  778.948608\n",
            "73  2020-04-29  sub_region  ...             73  778.723877\n",
            "74  2020-04-30  sub_region  ...             74  778.891602\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/media/plotly/test_plot_20_03adc1c1.plotly.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: op8okjem \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171814-op8okjem/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: op8okjem\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: kmhcvsx4 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: kmhcvsx4\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/kmhcvsx4\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/kmhcvsx4</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/kmhcvsx4\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmttaGN2c3g0OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/media/graph/graph_0_summary_a57114ef.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.668751269578934\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.3951389359103309\n",
            "The running loss is:\n",
            "28.521158926188946\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.0563392194884795\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.176970317959785\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.5250729747392513\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.591888493858278\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.24414401829104732\n",
            "1\n",
            "The running loss is:\n",
            "7.487370473332703\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.27731001753084084\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.26125293970108\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.1948612199889289\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['kmhcvsx4']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.058157247491181\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.22437619435152523\n",
            "1\n",
            "The running loss is:\n",
            "4.886977478861809\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.18099916588377069\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.049873136915267\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.29814344951538024\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.706313902512193\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.24838199638934047\n",
            "2\n",
            "Data saved to: \n",
            "28_May_202005_18PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_18PM_model.pth\n",
            "interpolate should be below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  755.005249\n",
            "66  2020-04-22  sub_region  ...             66  751.443115\n",
            "67  2020-04-23  sub_region  ...             67  753.844849\n",
            "68  2020-04-24  sub_region  ...             68  755.624390\n",
            "69  2020-04-25  sub_region  ...             69  755.841370\n",
            "70  2020-04-26  sub_region  ...             70  755.883606\n",
            "71  2020-04-27  sub_region  ...             71  752.995850\n",
            "72  2020-04-28  sub_region  ...             72  755.255066\n",
            "73  2020-04-29  sub_region  ...             73  751.328247\n",
            "74  2020-04-30  sub_region  ...             74  753.881714\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/media/plotly/test_plot_20_9c0dfd36.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: kmhcvsx4 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171832-kmhcvsx4/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: kmhcvsx4\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 4o5xdnw0 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 4o5xdnw0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/4o5xdnw0\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/4o5xdnw0</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/4o5xdnw0\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjRvNXhkbncwOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/media/graph/graph_0_summary_b776ceb7.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/wandb-history.jsonl\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "23.849574618041515\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.883317578445982\n",
            "The running loss is:\n",
            "20.568836465477943\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.7618087579806646\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.27038937062025\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7137181248377871\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.25364562869072\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.6390239121737303\n",
            "1\n",
            "The running loss is:\n",
            "14.78194186091423\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.5474793281820085\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.99204146116972\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.40711264670998965\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['4o5xdnw0']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "34.4778664149344\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "1.2769580153679405\n",
            "The running loss is:\n",
            "18.340780273079872\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.6792881582622174\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.76011623442173\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.6948191197933974\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.743604812771082\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.6942075856581882\n",
            "Data saved to: \n",
            "28_May_202005_18PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_18PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  351.287323\n",
            "66  2020-04-22  sub_region  ...             66  351.352722\n",
            "67  2020-04-23  sub_region  ...             67  351.261353\n",
            "68  2020-04-24  sub_region  ...             68  351.227020\n",
            "69  2020-04-25  sub_region  ...             69  351.256317\n",
            "70  2020-04-26  sub_region  ...             70  351.258667\n",
            "71  2020-04-27  sub_region  ...             71  351.388000\n",
            "72  2020-04-28  sub_region  ...             72  351.195374\n",
            "73  2020-04-29  sub_region  ...             73  351.413818\n",
            "74  2020-04-30  sub_region  ...             74  351.286987\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/media/plotly/test_plot_20_3234c249.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 4o5xdnw0 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171848-4o5xdnw0/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 4o5xdnw0\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: iyc8jxdt with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: iyc8jxdt\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/iyc8jxdt\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/iyc8jxdt</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/iyc8jxdt\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOml5YzhqeGR0OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/media/graph/graph_0_summary_e1e54220.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "23.785254307091236\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.8809353447070828\n",
            "The running loss is:\n",
            "20.365187123417854\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.7542661897562168\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.407820589840412\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7188081699940894\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.047366067767143\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.594346891398783\n",
            "1\n",
            "The running loss is:\n",
            "16.049381844699383\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.5944215498036809\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.695411691442132\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.3220522848682271\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['iyc8jxdt']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.989098764955997\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.4440406949983703\n",
            "The running loss is:\n",
            "12.975145380944014\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.48056094003496347\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.981494817882776\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.25857388214380655\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.491051498800516\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.27744635180742655\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_19PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_19PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  709.696960\n",
            "66  2020-04-22  sub_region  ...             66  709.161499\n",
            "67  2020-04-23  sub_region  ...             67  708.643188\n",
            "68  2020-04-24  sub_region  ...             68  708.597656\n",
            "69  2020-04-25  sub_region  ...             69  708.908020\n",
            "70  2020-04-26  sub_region  ...             70  708.755493\n",
            "71  2020-04-27  sub_region  ...             71  709.836182\n",
            "72  2020-04-28  sub_region  ...             72  708.476990\n",
            "73  2020-04-29  sub_region  ...             73  709.191528\n",
            "74  2020-04-30  sub_region  ...             74  708.709473\n",
            "\n",
            "[20 rows x 28 columns]"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/media/plotly/test_plot_20_509b02c0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/media\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: iyc8jxdt \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171903-iyc8jxdt/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: iyc8jxdt\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: ybklo6n3 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: ybklo6n3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/ybklo6n3\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/ybklo6n3</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/ybklo6n3\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnlia2xvNm4zOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171919-ybklo6n3/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171919-ybklo6n3/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171919-ybklo6n3/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171919-ybklo6n3/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171919-ybklo6n3/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171919-ybklo6n3/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171919-ybklo6n3/media/graph/graph_0_summary_36165fc0.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171919-ybklo6n3/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171919-ybklo6n3/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "25.31017677485943\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.9734683374945934\n",
            "The running loss is:\n",
            "17.691114902496338\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.6804274962498591\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171919-ybklo6n3/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171919-ybklo6n3/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.284134477376938\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.8186205568221899\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171919-ybklo6n3/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171919-ybklo6n3/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.811767756938934\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.5696833752668821\n",
            "The running loss is:\n",
            "12.015214286744595\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.46212362641325366\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171919-ybklo6n3/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171919-ybklo6n3/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.353260762989521\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.3982023370380585\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['ybklo6n3']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.667264841496944\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.2948948015960363\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171919-ybklo6n3/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171919-ybklo6n3/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_19PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_19PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171919-ybklo6n3/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171919-ybklo6n3/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171919-ybklo6n3/wandb-summary.json\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  318.850616\n",
            "66  2020-04-22  sub_region  ...             66  316.706177\n",
            "67  2020-04-23  sub_region  ...             67  320.013977\n",
            "68  2020-04-24  sub_region  ...             68  321.565765\n",
            "69  2020-04-25  sub_region  ...             69  321.352051\n",
            "70  2020-04-26  sub_region  ...             70  320.382446\n",
            "71  2020-04-27  sub_region  ...             71  318.602081\n",
            "72  2020-04-28  sub_region  ...             72  318.317017\n",
            "73  2020-04-29  sub_region  ...             73  318.346680\n",
            "74  2020-04-30  sub_region  ...             74  320.484741\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171919-ybklo6n3/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171919-ybklo6n3/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171919-ybklo6n3/wandb-summary.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171919-ybklo6n3/media/plotly/test_plot_14_b1c54b64.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171919-ybklo6n3/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171919-ybklo6n3/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: ybklo6n3 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171919-ybklo6n3/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171919-ybklo6n3/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171919-ybklo6n3/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171919-ybklo6n3/media/plotly/test_plot_all_15_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171919-ybklo6n3/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171919-ybklo6n3/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: ybklo6n3\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
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            "wandb: Agent Starting Run: 5ca6mu99 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 5ca6mu99\n"
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          "name": "stdout"
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          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/5ca6mu99\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/5ca6mu99</a><br/>\n",
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          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171937-5ca6mu99/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171937-5ca6mu99/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171937-5ca6mu99/media/graph/graph_0_summary_b0c413d6.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171937-5ca6mu99/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171937-5ca6mu99/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171937-5ca6mu99/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171937-5ca6mu99/wandb-history.jsonl\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "25.090075597167015\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.9650029075833467\n",
            "The running loss is:\n",
            "17.45224541425705\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.6712402082406558\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171937-5ca6mu99/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171937-5ca6mu99/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.34785506129265\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.8210713485112557\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171937-5ca6mu99/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171937-5ca6mu99/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.617094554007053\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.5621959443848866\n",
            "1\n",
            "The running loss is:\n",
            "12.857044279575348\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.4945017030605903\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171937-5ca6mu99/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171937-5ca6mu99/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.908861950039864\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.4195716134630717\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['5ca6mu99']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.671748608350754\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.2950672541673367\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171937-5ca6mu99/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171937-5ca6mu99/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.437005430459976\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.2475771319407683\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171937-5ca6mu99/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171937-5ca6mu99/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.865247845649719\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.3025095325249892\n",
            "1\n",
            "The running loss is:\n",
            "5.2042678743600845\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.2001641490138494\n",
            "Data saved to: \n",
            "28_May_202005_19PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171937-5ca6mu99/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171937-5ca6mu99/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_19PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  570.512756\n",
            "66  2020-04-22  sub_region  ...             66  571.143372\n",
            "67  2020-04-23  sub_region  ...             67  570.903015\n",
            "68  2020-04-24  sub_region  ...             68  570.691162\n",
            "69  2020-04-25  sub_region  ...             69  570.595093\n",
            "70  2020-04-26  sub_region  ...             70  570.591003\n",
            "71  2020-04-27  sub_region  ...             71  570.901611\n",
            "72  2020-04-28  sub_region  ...             72  570.663696\n",
            "73  2020-04-29  sub_region  ...             73  571.259827\n",
            "74  2020-04-30  sub_region  ...             74  571.026001\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171937-5ca6mu99/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171937-5ca6mu99/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171937-5ca6mu99/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171937-5ca6mu99/wandb-history.jsonl\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171937-5ca6mu99/media/plotly/test_plot_20_96e49c04.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171937-5ca6mu99/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171937-5ca6mu99/media/plotly\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 5ca6mu99 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171937-5ca6mu99/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171937-5ca6mu99/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171937-5ca6mu99/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171937-5ca6mu99/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171937-5ca6mu99/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171937-5ca6mu99/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 5ca6mu99\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: w8uacmny with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: w8uacmny\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/w8uacmny\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/w8uacmny</a><br/>\n",
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            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/w8uacmny\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnc4dWFjbW55OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171953-w8uacmny/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171953-w8uacmny/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171953-w8uacmny/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171953-w8uacmny/media/graph/graph_0_summary_96d1a8d8.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171953-w8uacmny/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171953-w8uacmny/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171953-w8uacmny/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171953-w8uacmny/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171953-w8uacmny/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "24.998789221048355\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.9614918931172445\n",
            "The running loss is:\n",
            "19.62556444108486\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.754829401580187\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171953-w8uacmny/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171953-w8uacmny/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.021592140197754\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.7315996976999136\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171953-w8uacmny/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171953-w8uacmny/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.53062754869461\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.5204087518728696\n",
            "The running loss is:\n",
            "11.939093425869942\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.45919590099499774\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171953-w8uacmny/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171953-w8uacmny/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.370361641049385\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.475783140040361\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['w8uacmny']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.105389013886452\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.388668808226402\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171953-w8uacmny/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171953-w8uacmny/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.374523870646954\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.20671245656334436\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171953-w8uacmny/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171953-w8uacmny/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.875691753812134\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.18752660591585132\n",
            "The running loss is:\n",
            "7.123469600221142\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.27397960000850546\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_20PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171953-w8uacmny/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171953-w8uacmny/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_20PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  697.734924\n",
            "66  2020-04-22  sub_region  ...             66  699.246887\n",
            "67  2020-04-23  sub_region  ...             67  698.447754\n",
            "68  2020-04-24  sub_region  ...             68  697.592102\n",
            "69  2020-04-25  sub_region  ...             69  697.349731\n",
            "70  2020-04-26  sub_region  ...             70  697.215332\n",
            "71  2020-04-27  sub_region  ...             71  698.537048\n",
            "72  2020-04-28  sub_region  ...             72  697.177002\n",
            "73  2020-04-29  sub_region  ...             73  699.459961\n",
            "74  2020-04-30  sub_region  ...             74  698.878723\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171953-w8uacmny/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171953-w8uacmny/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171953-w8uacmny/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171953-w8uacmny/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171953-w8uacmny/media/plotly/test_plot_20_70b23860.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171953-w8uacmny/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171953-w8uacmny/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: w8uacmny \n",
            "\n"
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          "name": "stdout"
        },
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          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171953-w8uacmny/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171953-w8uacmny/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171953-w8uacmny/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171953-w8uacmny/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171953-w8uacmny/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_171953-w8uacmny/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: w8uacmny\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: rv3jho0p with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: rv3jho0p\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/rv3jho0p\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/rv3jho0p</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/rv3jho0p\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnJ2M2pobzBwOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172009-rv3jho0p/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172009-rv3jho0p/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172009-rv3jho0p/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172009-rv3jho0p/media/graph/graph_0_summary_7995e82c.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172009-rv3jho0p/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172009-rv3jho0p/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172009-rv3jho0p/wandb-events.jsonl\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172009-rv3jho0p/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "24.827137678861618\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.9548899107254468\n",
            "The running loss is:\n",
            "19.719864428043365\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.758456324155514\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172009-rv3jho0p/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172009-rv3jho0p/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.63474379479885\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.7167209151845712\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172009-rv3jho0p/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172009-rv3jho0p/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.806637480854988\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.5310245184944227\n",
            "1\n",
            "The running loss is:\n",
            "11.331828974187374\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.4358395759302836\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172009-rv3jho0p/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172009-rv3jho0p/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.775343157351017\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.4144362752827314\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['rv3jho0p']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.32153557986021\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.3969821376869312\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172009-rv3jho0p/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172009-rv3jho0p/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.037654765415937\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.2706790294390745\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172009-rv3jho0p/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172009-rv3jho0p/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.718151992186904\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.5660827689302655\n",
            "1\n",
            "The running loss is:\n",
            "5.737017912324518\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.22065453508940452\n",
            "Data saved to: \n",
            "28_May_202005_20PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172009-rv3jho0p/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172009-rv3jho0p/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_20PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  632.474121\n",
            "66  2020-04-22  sub_region  ...             66  634.329102\n",
            "67  2020-04-23  sub_region  ...             67  632.773071\n",
            "68  2020-04-24  sub_region  ...             68  631.713074\n",
            "69  2020-04-25  sub_region  ...             69  631.196228\n",
            "70  2020-04-26  sub_region  ...             70  631.151917\n",
            "71  2020-04-27  sub_region  ...             71  632.160889\n",
            "72  2020-04-28  sub_region  ...             72  631.541016\n",
            "73  2020-04-29  sub_region  ...             73  633.977478\n",
            "74  2020-04-30  sub_region  ...             74  632.605713\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172009-rv3jho0p/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172009-rv3jho0p/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172009-rv3jho0p/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172009-rv3jho0p/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172009-rv3jho0p/media/plotly/test_plot_20_a4b0535b.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172009-rv3jho0p/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172009-rv3jho0p/media/plotly\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: rv3jho0p \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172009-rv3jho0p/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172009-rv3jho0p/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172009-rv3jho0p/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172009-rv3jho0p/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172009-rv3jho0p/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172009-rv3jho0p/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: rv3jho0p\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: yezsyz5y with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: yezsyz5y\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/yezsyz5y\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/yezsyz5y</a><br/>\n",
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        {
          "output_type": "stream",
          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/yezsyz5y\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnllenN5ejV5OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/media/graph/graph_0_summary_a3f207a1.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.056726850569248\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 0\n",
            "0.5734545303774732\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "46.208357490599155\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 1\n",
            "1.6502984818071127\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "29.032308392226696\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 2\n",
            "1.0368681568652391\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "28.841471418738365\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 3\n",
            "1.0300525506692273\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['yezsyz5y']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "28.338371301069856\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 4\n",
            "1.0120846893239235\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "28.69572694809176\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 5\n",
            "1.0248473910032772\n",
            "2\n",
            "The running loss is:\n",
            "19.646056853234768\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 6\n",
            "0.7016448876155275\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_20PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_20PM_model.pth\n",
            "interpolate should be below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  429.154022\n",
            "66  2020-04-22  sub_region  ...             66  428.729492\n",
            "67  2020-04-23  sub_region  ...             67  429.150635\n",
            "68  2020-04-24  sub_region  ...             68  429.542450\n",
            "69  2020-04-25  sub_region  ...             69  429.516174\n",
            "70  2020-04-26  sub_region  ...             70  429.553772\n",
            "71  2020-04-27  sub_region  ...             71  428.790649\n",
            "72  2020-04-28  sub_region  ...             72  429.774963\n",
            "73  2020-04-29  sub_region  ...             73  428.176636\n",
            "74  2020-04-30  sub_region  ...             74  428.942596\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/media/plotly/test_plot_14_bdfadf79.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: yezsyz5y \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/media/plotly/test_plot_all_15_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172025-yezsyz5y/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: yezsyz5y\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: lwpzskh5 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: lwpzskh5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/lwpzskh5\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/lwpzskh5</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/lwpzskh5\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmx3cHpza2g1OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/media/graph/graph_0_summary_7cbaf016.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.879746047779918\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 0\n",
            "0.4957052159921399\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "50.16468261182308\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 1\n",
            "1.7915958075651102\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "28.752860818058252\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 2\n",
            "1.0268878863592232\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "28.75844579562545\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 3\n",
            "1.027087349843766\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['lwpzskh5']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "28.228818399831653\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 4\n",
            "1.0081720857082732\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "28.275303000351414\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 5\n",
            "1.0098322500125505\n",
            "2\n",
            "The running loss is:\n",
            "19.328091056086123\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 6\n",
            "0.6902889662887901\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_20PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_20PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  429.017181\n",
            "66  2020-04-22  sub_region  ...             66  428.986389\n",
            "67  2020-04-23  sub_region  ...             67  429.089111\n",
            "68  2020-04-24  sub_region  ...             68  429.224731\n",
            "69  2020-04-25  sub_region  ...             69  429.164246\n",
            "70  2020-04-26  sub_region  ...             70  429.191895\n",
            "71  2020-04-27  sub_region  ...             71  428.905151\n",
            "72  2020-04-28  sub_region  ...             72  429.181915\n",
            "73  2020-04-29  sub_region  ...             73  428.645477\n",
            "74  2020-04-30  sub_region  ...             74  428.955353\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/wandb-summary.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/media/plotly/test_plot_14_562144b7.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/media\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: lwpzskh5 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/media/plotly/test_plot_all_15_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172041-lwpzskh5/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: lwpzskh5\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: s7nal4zt with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: s7nal4zt\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/s7nal4zt\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/s7nal4zt</a><br/>\n",
              "            "
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            "DEBUG:wandb.meta:probe for git information\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/s7nal4zt\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnM3bmFsNHp0OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172058-s7nal4zt/config.yaml\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172058-s7nal4zt/requirements.txt\n",
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          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.515796359628439\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.3524369022084607\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172058-s7nal4zt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172058-s7nal4zt/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "31.958917625248432\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.1836636157499418\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172058-s7nal4zt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172058-s7nal4zt/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.400996170938015\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.6444813396643709\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172058-s7nal4zt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172058-s7nal4zt/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.490230657160282\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.647786320635566\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['s7nal4zt']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172058-s7nal4zt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172058-s7nal4zt/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.88597560673952\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.6624435409903526\n",
            "The running loss is:\n",
            "17.275350011885166\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.6398277782179691\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172058-s7nal4zt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172058-s7nal4zt/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.80729231238365\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.6595293449030982\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172058-s7nal4zt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172058-s7nal4zt/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.339848961681128\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.6422166282104121\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172058-s7nal4zt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172058-s7nal4zt/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.749763071537018\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.6203615952421118\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172058-s7nal4zt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172058-s7nal4zt/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.160026656463742\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.6355565428319905\n",
            "Data saved to: \n",
            "28_May_202005_21PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172058-s7nal4zt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172058-s7nal4zt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172058-s7nal4zt/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "28_May_202005_21PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  452.964355\n",
            "66  2020-04-22  sub_region  ...             66  448.373505\n",
            "67  2020-04-23  sub_region  ...             67  451.731903\n",
            "68  2020-04-24  sub_region  ...             68  454.205078\n",
            "69  2020-04-25  sub_region  ...             69  454.852173\n",
            "70  2020-04-26  sub_region  ...             70  454.945068\n",
            "71  2020-04-27  sub_region  ...             71  451.557831\n",
            "72  2020-04-28  sub_region  ...             72  453.470245\n",
            "73  2020-04-29  sub_region  ...             73  448.514465\n",
            "74  2020-04-30  sub_region  ...             74  451.503876\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172058-s7nal4zt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172058-s7nal4zt/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172058-s7nal4zt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172058-s7nal4zt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172058-s7nal4zt/media/plotly/test_plot_20_da2b038c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172058-s7nal4zt/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172058-s7nal4zt/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: s7nal4zt \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172058-s7nal4zt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172058-s7nal4zt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172058-s7nal4zt/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172058-s7nal4zt/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172058-s7nal4zt/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172058-s7nal4zt/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: s7nal4zt\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: qnt4stp8 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: qnt4stp8\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/qnt4stp8\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/qnt4stp8</a><br/>\n",
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        {
          "output_type": "stream",
          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/qnt4stp8\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnFudDRzdHA4OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/media/graph/graph_0_summary_70f484d6.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.540501032024622\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.353351890074986\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "32.01947431452572\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.1859064560935453\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.433912321925163\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.6457004563675987\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.372653737664223\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.6434316199134897\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['qnt4stp8']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.937954049557447\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.6643686685021277\n",
            "The running loss is:\n",
            "17.54516338557005\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.649820866132224\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.718922704458237\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.6562563964614162\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.30660080164671\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.6409852148758041\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.718926943838596\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.6192195164384665\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.827449632808566\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.6232388752892062\n",
            "Data saved to: \n",
            "28_May_202005_21PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_21PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  457.992615\n",
            "66  2020-04-22  sub_region  ...             66  454.040710\n",
            "67  2020-04-23  sub_region  ...             67  456.812286\n",
            "68  2020-04-24  sub_region  ...             68  459.140167\n",
            "69  2020-04-25  sub_region  ...             69  459.434723\n",
            "70  2020-04-26  sub_region  ...             70  459.551544\n",
            "71  2020-04-27  sub_region  ...             71  456.045074\n",
            "72  2020-04-28  sub_region  ...             72  458.655548\n",
            "73  2020-04-29  sub_region  ...             73  453.452576\n",
            "74  2020-04-30  sub_region  ...             74  456.783661\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: qnt4stp8 \n",
            "\n"
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          "name": "stdout"
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          "output_type": "stream",
          "text": [
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/media/plotly/test_plot_20_c566c54f.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172119-qnt4stp8/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: qnt4stp8\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: tau1uhlr with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: tau1uhlr\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/tau1uhlr\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/tau1uhlr</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/tau1uhlr\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnRhdTF1aGxyOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
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          ],
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        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
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          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172140-tau1uhlr/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172140-tau1uhlr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172140-tau1uhlr/media/graph/graph_0_summary_d503964e.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172140-tau1uhlr/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172140-tau1uhlr/requirements.txt\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "31.62208504229784\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "1.17118833489992\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172140-tau1uhlr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172140-tau1uhlr/wandb-history.jsonl\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.408665716648102\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.7188394709869668\n",
            "1\n"
          ],
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        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172140-tau1uhlr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172140-tau1uhlr/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.407186299562454\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7558217147986094\n"
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          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172140-tau1uhlr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172140-tau1uhlr/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.506580412387848\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.759502978236587\n"
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          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['tau1uhlr']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172140-tau1uhlr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172140-tau1uhlr/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.30820331722498\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.7521556784157399\n",
            "The running loss is:\n",
            "19.309663198888302\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.7151727110699371\n"
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          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172140-tau1uhlr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172140-tau1uhlr/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "18.341759957373142\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.679324442865672\n"
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        {
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          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172140-tau1uhlr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172140-tau1uhlr/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.737579837441444\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.6569474013867201\n",
            "1\n"
          ],
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        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172140-tau1uhlr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172140-tau1uhlr/wandb-history.jsonl\n"
          ],
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        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.28192499279976\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.7141453701036947\n"
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        {
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          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172140-tau1uhlr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172140-tau1uhlr/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.758581429719925\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.694762275174812\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_21PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_21PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172140-tau1uhlr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172140-tau1uhlr/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172140-tau1uhlr/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  337.341278\n",
            "66  2020-04-22  sub_region  ...             66  337.345581\n",
            "67  2020-04-23  sub_region  ...             67  337.355377\n",
            "68  2020-04-24  sub_region  ...             68  337.360901\n",
            "69  2020-04-25  sub_region  ...             69  337.360565\n",
            "70  2020-04-26  sub_region  ...             70  337.355988\n",
            "71  2020-04-27  sub_region  ...             71  337.354065\n",
            "72  2020-04-28  sub_region  ...             72  337.343140\n",
            "73  2020-04-29  sub_region  ...             73  337.349548\n",
            "74  2020-04-30  sub_region  ...             74  337.355652\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172140-tau1uhlr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172140-tau1uhlr/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: tau1uhlr \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172140-tau1uhlr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172140-tau1uhlr/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172140-tau1uhlr/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172140-tau1uhlr/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172140-tau1uhlr/media/plotly/test_plot_20_e91edfe1.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172140-tau1uhlr/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172140-tau1uhlr/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172140-tau1uhlr/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: tau1uhlr\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 67mx7y58 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 67mx7y58\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/67mx7y58\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/67mx7y58</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/67mx7y58\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjY3bXg3eTU4OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/media/graph/graph_0_summary_7b6f7b4e.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "31.686953715980053\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "1.1735908783696316\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.326208144426346\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.7157854868306054\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.44809077680111\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7573366954370782\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.70396327972412\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.7668134548045971\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['67mx7y58']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.114036343991756\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.7449643090367317\n",
            "1\n",
            "The running loss is:\n",
            "19.89861784130335\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.7369858459741981\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.967432893812656\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.7024975145856539\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.17745415121317\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.7102760796745619\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.25604397803545\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.713186814001313\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.80481818318367\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.6964747475253211\n",
            "Data saved to: \n",
            "28_May_202005_22PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_22PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  353.958618\n",
            "66  2020-04-22  sub_region  ...             66  353.994934\n",
            "67  2020-04-23  sub_region  ...             67  353.966064\n",
            "68  2020-04-24  sub_region  ...             68  353.948608\n",
            "69  2020-04-25  sub_region  ...             69  353.950348\n",
            "70  2020-04-26  sub_region  ...             70  353.951172\n",
            "71  2020-04-27  sub_region  ...             71  353.985535\n",
            "72  2020-04-28  sub_region  ...             72  353.945618\n",
            "73  2020-04-29  sub_region  ...             73  354.007202\n",
            "74  2020-04-30  sub_region  ...             74  353.972107\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/media/plotly/test_plot_20_384740d4.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 67mx7y58 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172201-67mx7y58/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 67mx7y58\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: pxdi511t with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: pxdi511t\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/pxdi511t\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/pxdi511t</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/pxdi511t\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnB4ZGk1MTF0OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/media/graph/graph_0_summary_0b78d094.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "28.677166178822517\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "1.1029679299547122\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.185990750789642\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.737922721184217\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.213715583086014\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.777450599349462\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.579263865947723\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.5992024563826047\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['pxdi511t']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.614615187048912\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.6005621225788043\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.192182525992393\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.5458531740766305\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.893233135342598\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.6882012744362538\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.887526050209999\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.6110586942388461\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.056910991668701\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.50218888429495\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.211280450224876\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.39274155577787984\n",
            "2\n",
            "Data saved to: \n",
            "28_May_202005_22PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_22PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  409.355835\n",
            "66  2020-04-22  sub_region  ...             66  409.509674\n",
            "67  2020-04-23  sub_region  ...             67  409.471558\n",
            "68  2020-04-24  sub_region  ...             68  409.399261\n",
            "69  2020-04-25  sub_region  ...             69  409.354004\n",
            "70  2020-04-26  sub_region  ...             70  409.365173\n",
            "71  2020-04-27  sub_region  ...             71  409.424011\n",
            "72  2020-04-28  sub_region  ...             72  409.376404\n",
            "73  2020-04-29  sub_region  ...             73  409.552612\n",
            "74  2020-04-30  sub_region  ...             74  409.473022\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/media/plotly/test_plot_20_eefb9a0f.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: pxdi511t \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172222-pxdi511t/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: pxdi511t\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 7f4kxu00 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 7f4kxu00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/7f4kxu00\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/7f4kxu00</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/7f4kxu00\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjdmNGt4dTAwOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/media/graph/graph_0_summary_a08b7baa.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/wandb-summary.json\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "28.61015349626541\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "1.1003905190871313\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.129196166992188\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.7357383141150842\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.223093792796135\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.7778112997229283\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.592137679457664\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.599697603056064\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['7f4kxu00']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.741975575685501\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.5669990606032885\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.141681730747223\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.5439108357979701\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.65192748606205\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.6789202879254634\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.934842199087143\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.5359554691956594\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.86246071755886\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.4177869506753408\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.102634258568287\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.3501013176372418\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_22PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_22PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  475.105896\n",
            "66  2020-04-22  sub_region  ...             66  475.061249\n",
            "67  2020-04-23  sub_region  ...             67  475.085632\n",
            "68  2020-04-24  sub_region  ...             68  475.112213\n",
            "69  2020-04-25  sub_region  ...             69  475.114807\n",
            "70  2020-04-26  sub_region  ...             70  475.139618\n",
            "71  2020-04-27  sub_region  ...             71  475.079346\n",
            "72  2020-04-28  sub_region  ...             72  475.173126\n",
            "73  2020-04-29  sub_region  ...             73  475.043427\n",
            "74  2020-04-30  sub_region  ...             74  475.085358\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/media/plotly/test_plot_20_114ca43c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 7f4kxu00 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172243-7f4kxu00/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 7f4kxu00\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: yobxmwpd with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: yobxmwpd\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/yobxmwpd\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/yobxmwpd</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/yobxmwpd\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnlvYnhtd3BkOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/media/graph/graph_0_summary_68b4e924.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "26.287698671221733\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "1.0110653335085282\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.019511476159096\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.8469042875445806\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.7527959048748\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.7597229194182616\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.987582370638847\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.6149070142553403\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['yobxmwpd']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.31729331612587\n",
            "The number of items in train is: \n",
            "26\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The loss for epoch 4\n",
            "0.5891266660048411\n",
            "The running loss is:\n",
            "13.650041669607162\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.5250016026771985\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.348268389701843\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.5133949380654556\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.254892647266388\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.4328804864333226\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_23PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_23PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  347.929901\n",
            "66  2020-04-22  sub_region  ...             66  348.376862\n",
            "67  2020-04-23  sub_region  ...             67  348.171692\n",
            "68  2020-04-24  sub_region  ...             68  347.880554\n",
            "69  2020-04-25  sub_region  ...             69  347.761993\n",
            "70  2020-04-26  sub_region  ...             70  347.774658\n",
            "71  2020-04-27  sub_region  ...             71  348.046265\n",
            "72  2020-04-28  sub_region  ...             72  347.813934\n",
            "73  2020-04-29  sub_region  ...             73  348.664154\n",
            "74  2020-04-30  sub_region  ...             74  348.247253\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/media/plotly/test_plot_16_3f181331.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: yobxmwpd \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/media/plotly/test_plot_all_17_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172304-yobxmwpd/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: yobxmwpd\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: tuuqqbru with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: tuuqqbru\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/tuuqqbru\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/tuuqqbru</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/tuuqqbru\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnR1dXFxYnJ1OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/media/graph/graph_0_summary_9177faf0.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "26.332314282655716\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "1.0127813185636814\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.822422578930855\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.8393239453434944\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.969776809215546\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.7680683388159826\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.921830900013447\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.6123781115389787\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['tuuqqbru']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.494909837841988\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.5959580706862303\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.208502128720284\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.5464808511046263\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.151983082294464\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.621230118549787\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.41115165501833\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.4773519867314742\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.447976488620043\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.3633837111007709\n",
            "The running loss is:\n",
            "9.989659074693918\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.3842176567189969\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "Data saved to: \n",
            "28_May_202005_23PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_23PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/wandb-history.jsonl\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/config.yaml\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  578.508301\n",
            "66  2020-04-22  sub_region  ...             66  578.518188\n",
            "67  2020-04-23  sub_region  ...             67  578.516479\n",
            "68  2020-04-24  sub_region  ...             68  578.510986\n",
            "69  2020-04-25  sub_region  ...             69  578.503906\n",
            "70  2020-04-26  sub_region  ...             70  578.503906\n",
            "71  2020-04-27  sub_region  ...             71  578.500427\n",
            "72  2020-04-28  sub_region  ...             72  578.506470\n",
            "73  2020-04-29  sub_region  ...             73  578.516052\n",
            "74  2020-04-30  sub_region  ...             74  578.512817\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/media/plotly/test_plot_20_ae1f0e4f.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: tuuqqbru \n",
            "\n"
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172320-tuuqqbru/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: tuuqqbru\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
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          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: dm02dl9g with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: dm02dl9g\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/dm02dl9g\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/dm02dl9g</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/dm02dl9g\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmRtMDJkbDlnOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
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        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "15.545928395004012\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 0\n",
            "0.5552117283930004\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172341-dm02dl9g/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172341-dm02dl9g/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172341-dm02dl9g/media/graph/graph_0_summary_b9250aaf.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172341-dm02dl9g/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172341-dm02dl9g/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172341-dm02dl9g/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172341-dm02dl9g/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172341-dm02dl9g/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172341-dm02dl9g/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.661523299873807\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 1\n",
            "0.7736258321383502\n",
            "The running loss is:\n",
            "14.07579830638133\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 2\n",
            "0.5027070823707618\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172341-dm02dl9g/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172341-dm02dl9g/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.572435699170455\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 3\n",
            "0.4133012749703734\n",
            "1\n",
            "The running loss is:\n",
            "11.783770642941818\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 4\n",
            "0.42084895153363633\n",
            "The running loss is:\n",
            "9.61011728970334\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 5\n",
            "0.3432184746322621\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172341-dm02dl9g/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172341-dm02dl9g/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.171849727863446\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 6\n",
            "0.4704232045665516\n",
            "The running loss is:\n",
            "9.562567624612711\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 7\n",
            "0.34152027230759685\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172341-dm02dl9g/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172341-dm02dl9g/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "11.004484111152124\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 8\n",
            "0.3930172896840044\n",
            "2\n",
            "The running loss is:\n",
            "10.858391232584836\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 9\n",
            "0.38779968687802985\n",
            "Data saved to: \n",
            "28_May_202005_23PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.wandb_agent:Running runs: ['dm02dl9g']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_23PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172341-dm02dl9g/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172341-dm02dl9g/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172341-dm02dl9g/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  856.106934\n",
            "66  2020-04-22  sub_region  ...             66  898.349731\n",
            "67  2020-04-23  sub_region  ...             67  857.475891\n",
            "68  2020-04-24  sub_region  ...             68  861.468872\n",
            "69  2020-04-25  sub_region  ...             69  885.428162\n",
            "70  2020-04-26  sub_region  ...             70  897.812500\n",
            "71  2020-04-27  sub_region  ...             71  894.293274\n",
            "72  2020-04-28  sub_region  ...             72  929.067383\n",
            "73  2020-04-29  sub_region  ...             73  878.263550\n",
            "74  2020-04-30  sub_region  ...             74  869.549072\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172341-dm02dl9g/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172341-dm02dl9g/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172341-dm02dl9g/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172341-dm02dl9g/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172341-dm02dl9g/media/plotly/test_plot_20_314de4b9.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172341-dm02dl9g/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172341-dm02dl9g/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: dm02dl9g \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172341-dm02dl9g/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172341-dm02dl9g/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172341-dm02dl9g/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172341-dm02dl9g/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172341-dm02dl9g/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172341-dm02dl9g/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: dm02dl9g\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: b38vkw2p with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: b38vkw2p\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/b38vkw2p\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/b38vkw2p</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/b38vkw2p\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmIzOHZrdzJwOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "15.36638443195261\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 0\n",
            "0.5487994439983075\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172356-b38vkw2p/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172356-b38vkw2p/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172356-b38vkw2p/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172356-b38vkw2p/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172356-b38vkw2p/media/graph/graph_0_summary_933ff711.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172356-b38vkw2p/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172356-b38vkw2p/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172356-b38vkw2p/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172356-b38vkw2p/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.380546062253416\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 1\n",
            "0.6921623593661934\n",
            "The running loss is:\n",
            "13.36081693135202\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 2\n",
            "0.4771720332625721\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172356-b38vkw2p/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172356-b38vkw2p/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.953934513032436\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 3\n",
            "0.4269262326083013\n",
            "1\n",
            "The running loss is:\n",
            "12.163511937658768\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 4\n",
            "0.43441114063067027\n",
            "The running loss is:\n",
            "9.378338906506542\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 5\n",
            "0.33494067523237653\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172356-b38vkw2p/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172356-b38vkw2p/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.704894061491359\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 6\n",
            "0.4537462164818343\n",
            "The running loss is:\n",
            "11.437989893951453\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 7\n",
            "0.4084996390696948\n",
            "1\n",
            "The running loss is:\n",
            "11.367679933318868\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 8\n",
            "0.40598856904710245\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172356-b38vkw2p/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172356-b38vkw2p/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.42585400538519\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 9\n",
            "0.40806621447804253\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_24PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.wandb_agent:Running runs: ['b38vkw2p']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_24PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172356-b38vkw2p/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172356-b38vkw2p/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172356-b38vkw2p/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  802.753906\n",
            "66  2020-04-22  sub_region  ...             66  877.084839\n",
            "67  2020-04-23  sub_region  ...             67  830.681274\n",
            "68  2020-04-24  sub_region  ...             68  816.030273\n",
            "69  2020-04-25  sub_region  ...             69  821.921509\n",
            "70  2020-04-26  sub_region  ...             70  826.182373\n",
            "71  2020-04-27  sub_region  ...             71  846.830322\n",
            "72  2020-04-28  sub_region  ...             72  856.710266\n",
            "73  2020-04-29  sub_region  ...             73  857.412720\n",
            "74  2020-04-30  sub_region  ...             74  841.012085\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172356-b38vkw2p/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172356-b38vkw2p/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172356-b38vkw2p/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172356-b38vkw2p/wandb-summary.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172356-b38vkw2p/media/plotly/test_plot_20_e30096eb.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172356-b38vkw2p/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172356-b38vkw2p/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: b38vkw2p \n",
            "\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172356-b38vkw2p/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172356-b38vkw2p/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172356-b38vkw2p/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172356-b38vkw2p/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172356-b38vkw2p/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172356-b38vkw2p/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: b38vkw2p\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: aj14ok4m with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: aj14ok4m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/aj14ok4m\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/aj14ok4m</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/aj14ok4m\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmFqMTRvazRtOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "10.473457891494036\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.38790584783311244\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172412-aj14ok4m/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172412-aj14ok4m/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172412-aj14ok4m/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172412-aj14ok4m/media/graph/graph_0_summary_321f1f13.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172412-aj14ok4m/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172412-aj14ok4m/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172412-aj14ok4m/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172412-aj14ok4m/media\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.50141921825707\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.4630155266021137\n",
            "1\n",
            "The running loss is:\n",
            "12.783501834142953\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.47346303089418346\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172412-aj14ok4m/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172412-aj14ok4m/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.422599470242858\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.20083701741640214\n",
            "1\n",
            "The running loss is:\n",
            "5.399664411786944\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.19998757080692384\n",
            "The running loss is:\n",
            "4.413807349279523\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.16347434626961196\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172412-aj14ok4m/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172412-aj14ok4m/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.564219214487821\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.16904515609214152\n",
            "The running loss is:\n",
            "4.552914118394256\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.16862644882941688\n",
            "1\n",
            "The running loss is:\n",
            "4.2418790506199\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.15710663150444074\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172412-aj14ok4m/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172412-aj14ok4m/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "2\n",
            "The running loss is:\n",
            "4.331287162844092\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.16041804306829968\n",
            "Data saved to: \n",
            "28_May_202005_24PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.wandb_agent:Running runs: ['aj14ok4m']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_24PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172412-aj14ok4m/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172412-aj14ok4m/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172412-aj14ok4m/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  839.469238\n",
            "66  2020-04-22  sub_region  ...             66  751.845520\n",
            "67  2020-04-23  sub_region  ...             67  796.254761\n",
            "68  2020-04-24  sub_region  ...             68  840.047058\n",
            "69  2020-04-25  sub_region  ...             69  856.757812\n",
            "70  2020-04-26  sub_region  ...             70  857.516724\n",
            "71  2020-04-27  sub_region  ...             71  805.222778\n",
            "72  2020-04-28  sub_region  ...             72  846.005188\n",
            "73  2020-04-29  sub_region  ...             73  746.322815\n",
            "74  2020-04-30  sub_region  ...             74  799.226501\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172412-aj14ok4m/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172412-aj14ok4m/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172412-aj14ok4m/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172412-aj14ok4m/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172412-aj14ok4m/media/plotly/test_plot_20_908a465b.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172412-aj14ok4m/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172412-aj14ok4m/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: aj14ok4m \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172412-aj14ok4m/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172412-aj14ok4m/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172412-aj14ok4m/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172412-aj14ok4m/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172412-aj14ok4m/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172412-aj14ok4m/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: aj14ok4m\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: ymutcajd with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: ymutcajd\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/ymutcajd\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/ymutcajd</a><br/>\n",
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/ymutcajd\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnltdXRjYWpkOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "10.56319503299892\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.39122944566662665\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172428-ymutcajd/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172428-ymutcajd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172428-ymutcajd/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172428-ymutcajd/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172428-ymutcajd/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172428-ymutcajd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172428-ymutcajd/media/graph/graph_0_summary_5dee85ff.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172428-ymutcajd/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172428-ymutcajd/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.365171766839921\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.45796932469777485\n",
            "1\n",
            "The running loss is:\n",
            "10.063402196858078\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.37271859988363254\n",
            "The running loss is:\n",
            "5.191733076237142\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172428-ymutcajd/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The number of items in train is: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172428-ymutcajd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "27\n",
            "The loss for epoch 3\n",
            "0.19228641023100526\n",
            "1\n",
            "The running loss is:\n",
            "5.048446481116116\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.1869794993005969\n",
            "The running loss is:\n",
            "4.6334264650940895\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.17160838759607738\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172428-ymutcajd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172428-ymutcajd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.809368921909481\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.17812477488553635\n",
            "The running loss is:\n",
            "4.469904407858849\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.16555201510588327\n",
            "1\n",
            "The running loss is:\n",
            "4.4312631585635245\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.164120857724575\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172428-ymutcajd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172428-ymutcajd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.365964470198378\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.1617023877851251\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_24PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_24PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['ymutcajd']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172428-ymutcajd/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172428-ymutcajd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172428-ymutcajd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  820.661011\n",
            "66  2020-04-22  sub_region  ...             66  731.493591\n",
            "67  2020-04-23  sub_region  ...             67  777.978394\n",
            "68  2020-04-24  sub_region  ...             68  817.230530\n",
            "69  2020-04-25  sub_region  ...             69  833.734131\n",
            "70  2020-04-26  sub_region  ...             70  830.960205\n",
            "71  2020-04-27  sub_region  ...             71  781.981934\n",
            "72  2020-04-28  sub_region  ...             72  821.894836\n",
            "73  2020-04-29  sub_region  ...             73  732.720215\n",
            "74  2020-04-30  sub_region  ...             74  782.010132\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172428-ymutcajd/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172428-ymutcajd/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172428-ymutcajd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172428-ymutcajd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172428-ymutcajd/media/plotly/test_plot_20_a6760719.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172428-ymutcajd/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172428-ymutcajd/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: ymutcajd \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172428-ymutcajd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172428-ymutcajd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172428-ymutcajd/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172428-ymutcajd/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172428-ymutcajd/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172428-ymutcajd/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: ymutcajd\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: hzw7h1qu with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: hzw7h1qu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/hzw7h1qu\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/hzw7h1qu</a><br/>\n",
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          "metadata": {
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          "output_type": "stream",
          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/hzw7h1qu\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmh6dzdoMXF1OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "22.41447990387678\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.8301659223658068\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172444-hzw7h1qu/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172444-hzw7h1qu/media/graph/graph_0_summary_6d156013.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172444-hzw7h1qu/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172444-hzw7h1qu/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172444-hzw7h1qu/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172444-hzw7h1qu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172444-hzw7h1qu/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172444-hzw7h1qu/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172444-hzw7h1qu/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.35543255507946\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.7539049094473874\n",
            "1\n",
            "The running loss is:\n",
            "19.27651271224022\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7139449152681563\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172444-hzw7h1qu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172444-hzw7h1qu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.351722501218319\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.4574712037488266\n",
            "1\n",
            "The running loss is:\n",
            "9.813126027584076\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.36344911213274356\n",
            "The running loss is:\n",
            "6.200845863670111\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.2296609579137078\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172444-hzw7h1qu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172444-hzw7h1qu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.99026171118021\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.2218615448585263\n",
            "The running loss is:\n",
            "5.51112406142056\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.20411570597853926\n",
            "1\n",
            "The running loss is:\n",
            "5.962472440674901\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.22083231261758893\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172444-hzw7h1qu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172444-hzw7h1qu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.207641955465078\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.1928756279801881\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_24PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_24PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['hzw7h1qu']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172444-hzw7h1qu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172444-hzw7h1qu/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172444-hzw7h1qu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  731.740784\n",
            "66  2020-04-22  sub_region  ...             66  738.606201\n",
            "67  2020-04-23  sub_region  ...             67  737.777405\n",
            "68  2020-04-24  sub_region  ...             68  726.210022\n",
            "69  2020-04-25  sub_region  ...             69  719.908691\n",
            "70  2020-04-26  sub_region  ...             70  719.150635\n",
            "71  2020-04-27  sub_region  ...             71  720.189941\n",
            "72  2020-04-28  sub_region  ...             72  731.834473\n",
            "73  2020-04-29  sub_region  ...             73  735.288940\n",
            "74  2020-04-30  sub_region  ...             74  731.156189\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172444-hzw7h1qu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172444-hzw7h1qu/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172444-hzw7h1qu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172444-hzw7h1qu/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172444-hzw7h1qu/media/plotly/test_plot_20_3857be08.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172444-hzw7h1qu/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172444-hzw7h1qu/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: hzw7h1qu \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172444-hzw7h1qu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172444-hzw7h1qu/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172444-hzw7h1qu/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172444-hzw7h1qu/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172444-hzw7h1qu/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172444-hzw7h1qu/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: hzw7h1qu\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 36ic7346 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 36ic7346\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/36ic7346\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/36ic7346</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/36ic7346\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjM2aWM3MzQ2OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "22.289740599691868\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.8255459481367359\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172500-36ic7346/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172500-36ic7346/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172500-36ic7346/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172500-36ic7346/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172500-36ic7346/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172500-36ic7346/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172500-36ic7346/media/graph/graph_0_summary_3b05caf2.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172500-36ic7346/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172500-36ic7346/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.721937209367752\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.7674791559025094\n",
            "1\n",
            "The running loss is:\n",
            "19.501514554023743\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7222783168156942\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172500-36ic7346/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172500-36ic7346/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.022847175598145\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.4823276731703017\n",
            "1\n",
            "The running loss is:\n",
            "9.215275160968304\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.3413064874432705\n",
            "2\n",
            "The running loss is:\n",
            "6.453644126653671\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.23902385654272856\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172500-36ic7346/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172500-36ic7346/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.3195782247930765\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.23405845277011395\n",
            "1\n",
            "The running loss is:\n",
            "5.538634759373963\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.20513462071755417\n",
            "The running loss is:\n",
            "6.0211376789957285\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.22300509922206402\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172500-36ic7346/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172500-36ic7346/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.4065384501591325\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.2002421648207086\n",
            "Data saved to: \n",
            "28_May_202005_25PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_25PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['36ic7346']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  723.085754\n",
            "66  2020-04-22  sub_region  ...             66  731.862305\n",
            "67  2020-04-23  sub_region  ...             67  732.741089\n",
            "68  2020-04-24  sub_region  ...             68  723.526733\n",
            "69  2020-04-25  sub_region  ...             69  715.763306\n",
            "70  2020-04-26  sub_region  ...             70  713.154541\n",
            "71  2020-04-27  sub_region  ...             71  712.077576\n",
            "72  2020-04-28  sub_region  ...             72  725.158813\n",
            "73  2020-04-29  sub_region  ...             73  729.568176\n",
            "74  2020-04-30  sub_region  ...             74  726.045166\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172500-36ic7346/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172500-36ic7346/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172500-36ic7346/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172500-36ic7346/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172500-36ic7346/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172500-36ic7346/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172500-36ic7346/media/plotly/test_plot_20_967a32da.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172500-36ic7346/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172500-36ic7346/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 36ic7346 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172500-36ic7346/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172500-36ic7346/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172500-36ic7346/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172500-36ic7346/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172500-36ic7346/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172500-36ic7346/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 36ic7346\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: yndbm4kr with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: yndbm4kr\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/yndbm4kr\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/yndbm4kr</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/yndbm4kr\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnluZGJtNGtyOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "17.69617347419262\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.6806220566997161\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172511-yndbm4kr/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172511-yndbm4kr/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172511-yndbm4kr/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172511-yndbm4kr/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172511-yndbm4kr/media/graph/graph_0_summary_a573ac99.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172511-yndbm4kr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172511-yndbm4kr/wandb-events.jsonl\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.942448504269123\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.4977864809334278\n",
            "1\n",
            "The running loss is:\n",
            "11.007499657571316\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.4233653714450506\n",
            "2\n",
            "The running loss is:\n",
            "7.731042757630348\n",
            "The number of items in train is: \n",
            "26\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172511-yndbm4kr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172511-yndbm4kr/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The loss for epoch 3\n",
            "0.297347798370398\n",
            "The running loss is:\n",
            "4.677496463060379\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.1799037101177069\n",
            "1\n",
            "The running loss is:\n",
            "3.537908681668341\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.13607341083339775\n",
            "The running loss is:\n",
            "3.6070269970223308\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.13873180757778195\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172511-yndbm4kr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172511-yndbm4kr/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "5.469477290287614\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.21036451116490823\n",
            "2\n",
            "The running loss is:\n",
            "4.373710255138576\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.16821962519763753\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172511-yndbm4kr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172511-yndbm4kr/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.385507521219552\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.168673366200752\n",
            "Data saved to: \n",
            "28_May_202005_25PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_25PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['yndbm4kr']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172511-yndbm4kr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172511-yndbm4kr/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172511-yndbm4kr/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  641.062012\n",
            "66  2020-04-22  sub_region  ...             66  649.469788\n",
            "67  2020-04-23  sub_region  ...             67  662.655884\n",
            "68  2020-04-24  sub_region  ...             68  663.000610\n",
            "69  2020-04-25  sub_region  ...             69  652.813110\n",
            "70  2020-04-26  sub_region  ...             70  647.735291\n",
            "71  2020-04-27  sub_region  ...             71  634.131409\n",
            "72  2020-04-28  sub_region  ...             72  641.305664\n",
            "73  2020-04-29  sub_region  ...             73  641.875549\n",
            "74  2020-04-30  sub_region  ...             74  656.309570\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172511-yndbm4kr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172511-yndbm4kr/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172511-yndbm4kr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172511-yndbm4kr/wandb-history.jsonl\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172511-yndbm4kr/media/plotly/test_plot_20_7320596b.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172511-yndbm4kr/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172511-yndbm4kr/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: yndbm4kr \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172511-yndbm4kr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172511-yndbm4kr/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172511-yndbm4kr/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172511-yndbm4kr/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172511-yndbm4kr/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172511-yndbm4kr/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: yndbm4kr\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: y2e0r61g with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: y2e0r61g\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/y2e0r61g\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/y2e0r61g</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/y2e0r61g\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnkyZTByNjFnOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "17.936274334788322\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.6898567051841662\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172527-y2e0r61g/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172527-y2e0r61g/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172527-y2e0r61g/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172527-y2e0r61g/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172527-y2e0r61g/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172527-y2e0r61g/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172527-y2e0r61g/media/graph/graph_0_summary_e6593ad8.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172527-y2e0r61g/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172527-y2e0r61g/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.563346169888973\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.48320562191880667\n",
            "1\n",
            "The running loss is:\n",
            "10.63600341975689\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.4090770546060342\n",
            "2\n",
            "The running loss is:\n",
            "7.032686905935407\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.27048795792059255\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172527-y2e0r61g/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172527-y2e0r61g/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.1291360929608345\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.1588129266523398\n",
            "1\n",
            "The running loss is:\n",
            "3.5215289955958724\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.13544342290753356\n",
            "The running loss is:\n",
            "4.55407845415175\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172527-y2e0r61g/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The number of items in train is: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172527-y2e0r61g/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "26\n",
            "The loss for epoch 6\n",
            "0.17515686362122113\n",
            "1\n",
            "The running loss is:\n",
            "3.83646054379642\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.14755617476140076\n",
            "2\n",
            "The running loss is:\n",
            "4.684140918310732\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.18015926608887428\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172527-y2e0r61g/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172527-y2e0r61g/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.284909620881081\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.28018883157234925\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_25PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_25PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['y2e0r61g']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172527-y2e0r61g/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172527-y2e0r61g/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172527-y2e0r61g/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  811.324219\n",
            "66  2020-04-22  sub_region  ...             66  801.534241\n",
            "67  2020-04-23  sub_region  ...             67  837.117920\n",
            "68  2020-04-24  sub_region  ...             68  854.320984\n",
            "69  2020-04-25  sub_region  ...             69  834.952698\n",
            "70  2020-04-26  sub_region  ...             70  831.260376\n",
            "71  2020-04-27  sub_region  ...             71  782.874146\n",
            "72  2020-04-28  sub_region  ...             72  832.120972\n",
            "73  2020-04-29  sub_region  ...             73  785.114258\n",
            "74  2020-04-30  sub_region  ...             74  817.378418\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172527-y2e0r61g/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172527-y2e0r61g/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172527-y2e0r61g/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172527-y2e0r61g/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172527-y2e0r61g/media/plotly/test_plot_20_d2368e1b.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172527-y2e0r61g/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172527-y2e0r61g/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: y2e0r61g \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172527-y2e0r61g/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172527-y2e0r61g/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172527-y2e0r61g/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172527-y2e0r61g/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172527-y2e0r61g/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172527-y2e0r61g/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: y2e0r61g\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: gzism3qq with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: gzism3qq\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/gzism3qq\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/gzism3qq</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/gzism3qq\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmd6aXNtM3FxOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "16.614094384014606\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.639003630154408\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172543-gzism3qq/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172543-gzism3qq/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172543-gzism3qq/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172543-gzism3qq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172543-gzism3qq/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172543-gzism3qq/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172543-gzism3qq/media/graph/graph_0_summary_9dd2c671.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172543-gzism3qq/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172543-gzism3qq/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.784377932548523\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.49170684355955857\n",
            "1\n",
            "The running loss is:\n",
            "10.317914854735136\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.3968428790282745\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172543-gzism3qq/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172543-gzism3qq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "8.098226331174374\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.31147024350670666\n",
            "1\n",
            "The running loss is:\n",
            "5.721522863255814\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.22005857166368514\n",
            "The running loss is:\n",
            "4.689897304400802\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.180380665553877\n",
            "The running loss is:\n",
            "6.495792439207435\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172543-gzism3qq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172543-gzism3qq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.24983817073874748\n",
            "1\n",
            "The running loss is:\n",
            "5.418599771335721\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.20840768351291233\n",
            "The running loss is:\n",
            "4.195066409185529\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.16134870804559726\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172543-gzism3qq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172543-gzism3qq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.375888114795089\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.2067649274921188\n",
            "2\n",
            "Data saved to: \n",
            "28_May_202005_25PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_25PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['gzism3qq']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172543-gzism3qq/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172543-gzism3qq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172543-gzism3qq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  730.216797\n",
            "66  2020-04-22  sub_region  ...             66  741.966797\n",
            "67  2020-04-23  sub_region  ...             67  738.005005\n",
            "68  2020-04-24  sub_region  ...             68  724.475220\n",
            "69  2020-04-25  sub_region  ...             69  719.175964\n",
            "70  2020-04-26  sub_region  ...             70  717.805908\n",
            "71  2020-04-27  sub_region  ...             71  714.661133\n",
            "72  2020-04-28  sub_region  ...             72  731.062378\n",
            "73  2020-04-29  sub_region  ...             73  725.214966\n",
            "74  2020-04-30  sub_region  ...             74  723.802612\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172543-gzism3qq/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172543-gzism3qq/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172543-gzism3qq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172543-gzism3qq/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172543-gzism3qq/media/plotly/test_plot_20_54bf2aea.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172543-gzism3qq/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172543-gzism3qq/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: gzism3qq \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172543-gzism3qq/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172543-gzism3qq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172543-gzism3qq/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172543-gzism3qq/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172543-gzism3qq/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172543-gzism3qq/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: gzism3qq\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
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          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: lvl9ftc6 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: lvl9ftc6\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/lvl9ftc6\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/lvl9ftc6</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/lvl9ftc6\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmx2bDlmdGM2OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
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        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "16.377284348011017\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.6298955518465775\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172559-lvl9ftc6/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172559-lvl9ftc6/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172559-lvl9ftc6/media/graph/graph_0_summary_5ef9dede.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172559-lvl9ftc6/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172559-lvl9ftc6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172559-lvl9ftc6/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172559-lvl9ftc6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172559-lvl9ftc6/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172559-lvl9ftc6/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.166790530085564\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.5448765588494447\n",
            "The running loss is:\n",
            "9.428598899394274\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.3626384192074721\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172559-lvl9ftc6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172559-lvl9ftc6/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.6330753564834595\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.2551182829416715\n",
            "2\n",
            "The running loss is:\n",
            "5.67338194232434\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.2182069977817054\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_26PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_26PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172559-lvl9ftc6/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172559-lvl9ftc6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172559-lvl9ftc6/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  339.937469\n",
            "66  2020-04-22  sub_region  ...             66  348.662567\n",
            "67  2020-04-23  sub_region  ...             67  321.414612\n",
            "68  2020-04-24  sub_region  ...             68  302.783997\n",
            "69  2020-04-25  sub_region  ...             69  324.099854\n",
            "70  2020-04-26  sub_region  ...             70  317.279297\n",
            "71  2020-04-27  sub_region  ...             71  371.108948\n",
            "72  2020-04-28  sub_region  ...             72  297.625122\n",
            "73  2020-04-29  sub_region  ...             73  384.647125\n",
            "74  2020-04-30  sub_region  ...             74  332.773010\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172559-lvl9ftc6/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172559-lvl9ftc6/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.wandb_agent:Running runs: ['lvl9ftc6']\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172559-lvl9ftc6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172559-lvl9ftc6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172559-lvl9ftc6/media/plotly/test_plot_10_807ad6e3.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172559-lvl9ftc6/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172559-lvl9ftc6/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: lvl9ftc6 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172559-lvl9ftc6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172559-lvl9ftc6/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172559-lvl9ftc6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172559-lvl9ftc6/media/plotly/test_plot_all_11_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172559-lvl9ftc6/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172559-lvl9ftc6/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: lvl9ftc6\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: acomglw4 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: acomglw4\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/acomglw4\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/acomglw4</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/acomglw4\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmFjb21nbHc0OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172610-acomglw4/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172610-acomglw4/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172610-acomglw4/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172610-acomglw4/media/graph/graph_0_summary_0047ac6a.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172610-acomglw4/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172610-acomglw4/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172610-acomglw4/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172610-acomglw4/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172610-acomglw4/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.114518700167537\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 0\n",
            "0.5755185250059834\n",
            "The running loss is:\n",
            "40.93923968449235\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 1\n",
            "1.462115703017584\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172610-acomglw4/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172610-acomglw4/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.72650630073622\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 2\n",
            "0.6330895107405793\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172610-acomglw4/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172610-acomglw4/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.82603452168405\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 3\n",
            "0.4937869472030018\n",
            "The running loss is:\n",
            "12.235369089990854\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 4\n",
            "0.43697746749967337\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172610-acomglw4/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172610-acomglw4/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.42129836557433\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 5\n",
            "0.4436177987705118\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['acomglw4']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.593332045944408\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 6\n",
            "0.4140475730694431\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172610-acomglw4/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172610-acomglw4/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.022986069088802\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 7\n",
            "0.42939235961031436\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172610-acomglw4/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172610-acomglw4/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.050711158488411\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 8\n",
            "0.3946682556603004\n",
            "2\n",
            "The running loss is:\n",
            "10.95676115965398\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 9\n",
            "0.3913128985590707\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_26PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172610-acomglw4/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172610-acomglw4/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_26PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  837.403687\n",
            "66  2020-04-22  sub_region  ...             66  830.898865\n",
            "67  2020-04-23  sub_region  ...             67  824.932678\n",
            "68  2020-04-24  sub_region  ...             68  836.498291\n",
            "69  2020-04-25  sub_region  ...             69  841.817627\n",
            "70  2020-04-26  sub_region  ...             70  846.289368\n",
            "71  2020-04-27  sub_region  ...             71  830.351929\n",
            "72  2020-04-28  sub_region  ...             72  852.223999\n",
            "73  2020-04-29  sub_region  ...             73  823.185059\n",
            "74  2020-04-30  sub_region  ...             74  832.774414\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172610-acomglw4/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172610-acomglw4/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172610-acomglw4/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172610-acomglw4/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172610-acomglw4/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172610-acomglw4/media/plotly/test_plot_20_754d0a08.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172610-acomglw4/media\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172610-acomglw4/media/plotly\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: acomglw4 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172610-acomglw4/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172610-acomglw4/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172610-acomglw4/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172610-acomglw4/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172610-acomglw4/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
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            "INFO:wandb.wandb_agent:Cleaning up finished run: acomglw4\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
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            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
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            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
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            "wandb: Agent Starting Run: ir0x5ngg with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: ir0x5ngg\n"
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          "name": "stdout"
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            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/ir0x5ngg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/ir0x5ngg</a><br/>\n",
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          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172625-ir0x5ngg/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172625-ir0x5ngg/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172625-ir0x5ngg/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172625-ir0x5ngg/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172625-ir0x5ngg/media/graph/graph_0_summary_b50c2bff.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172625-ir0x5ngg/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172625-ir0x5ngg/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172625-ir0x5ngg/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172625-ir0x5ngg/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.39689282909967\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 0\n",
            "0.5498890296107025\n",
            "The running loss is:\n",
            "44.767726235091686\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 1\n",
            "1.5988473655389888\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172625-ir0x5ngg/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172625-ir0x5ngg/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.245736518874764\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 2\n",
            "0.794490589959813\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172625-ir0x5ngg/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172625-ir0x5ngg/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.605747589841485\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 3\n",
            "0.5573481282086244\n",
            "2\n",
            "The running loss is:\n",
            "18.130321642383933\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 4\n",
            "0.6475114872279976\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_26PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172625-ir0x5ngg/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172625-ir0x5ngg/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_26PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['ir0x5ngg']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  410.513519\n",
            "66  2020-04-22  sub_region  ...             66  402.391907\n",
            "67  2020-04-23  sub_region  ...             67  409.347412\n",
            "68  2020-04-24  sub_region  ...             68  416.087708\n",
            "69  2020-04-25  sub_region  ...             69  416.266327\n",
            "70  2020-04-26  sub_region  ...             70  415.997223\n",
            "71  2020-04-27  sub_region  ...             71  405.508575\n",
            "72  2020-04-28  sub_region  ...             72  415.981689\n",
            "73  2020-04-29  sub_region  ...             73  393.018555\n",
            "74  2020-04-30  sub_region  ...             74  405.671448\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172625-ir0x5ngg/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172625-ir0x5ngg/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: ir0x5ngg \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172625-ir0x5ngg/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172625-ir0x5ngg/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172625-ir0x5ngg/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172625-ir0x5ngg/media/plotly/test_plot_10_d4fb56b9.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172625-ir0x5ngg/media/plotly/test_plot_all_11_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172625-ir0x5ngg/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172625-ir0x5ngg/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172625-ir0x5ngg/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: ir0x5ngg\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: fjt2fktl with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: fjt2fktl\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/fjt2fktl\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/fjt2fktl</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/fjt2fktl\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmZqdDJma3RsOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172636-fjt2fktl/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172636-fjt2fktl/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172636-fjt2fktl/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172636-fjt2fktl/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172636-fjt2fktl/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172636-fjt2fktl/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172636-fjt2fktl/media/graph/graph_0_summary_03895c49.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172636-fjt2fktl/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172636-fjt2fktl/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.718430198729038\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.3969788962492236\n",
            "The running loss is:\n",
            "28.698574017733335\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.0629101488049384\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172636-fjt2fktl/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172636-fjt2fktl/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.018407717347145\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.5932743599017462\n",
            "1\n",
            "The running loss is:\n",
            "10.534781254827976\n",
            "The number of items in train is: \n",
            "27\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172636-fjt2fktl/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The loss for epoch 3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172636-fjt2fktl/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0.39017708351214725\n",
            "2\n",
            "The running loss is:\n",
            "5.385364955291152\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.1994579613070797\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172636-fjt2fktl/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172636-fjt2fktl/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.625352939590812\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.28242047924410413\n",
            "1\n",
            "The running loss is:\n",
            "6.7238668041536584\n",
            "The number of items in train is: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['fjt2fktl']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "27\n",
            "The loss for epoch 6\n",
            "0.2490321038575429\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172636-fjt2fktl/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172636-fjt2fktl/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.8772421116009355\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.29174970783707166\n",
            "The running loss is:\n",
            "7.317102946341038\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172636-fjt2fktl/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The number of items in train is: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172636-fjt2fktl/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "27\n",
            "The loss for epoch 8\n",
            "0.27100381282744584\n",
            "1\n",
            "The running loss is:\n",
            "5.420254968106747\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.20075018400395359\n",
            "Data saved to: \n",
            "28_May_202005_26PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172636-fjt2fktl/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172636-fjt2fktl/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_26PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  779.043823\n",
            "66  2020-04-22  sub_region  ...             66  778.755005\n",
            "67  2020-04-23  sub_region  ...             67  778.881592\n",
            "68  2020-04-24  sub_region  ...             68  779.025024\n",
            "69  2020-04-25  sub_region  ...             69  779.112366\n",
            "70  2020-04-26  sub_region  ...             70  779.083008\n",
            "71  2020-04-27  sub_region  ...             71  778.987671\n",
            "72  2020-04-28  sub_region  ...             72  778.948608\n",
            "73  2020-04-29  sub_region  ...             73  778.723877\n",
            "74  2020-04-30  sub_region  ...             74  778.891602\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172636-fjt2fktl/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172636-fjt2fktl/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: fjt2fktl \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172636-fjt2fktl/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172636-fjt2fktl/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172636-fjt2fktl/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172636-fjt2fktl/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172636-fjt2fktl/media/plotly/test_plot_20_03adc1c1.plotly.json\n",
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            "INFO:wandb.wandb_agent:Cleaning up finished run: fjt2fktl\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
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            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
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            "wandb: Agent Starting Run: jt4p6a4c with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: jt4p6a4c\n"
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          "name": "stdout"
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          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/jt4p6a4c\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/jt4p6a4c</a><br/>\n",
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            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172652-jt4p6a4c/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172652-jt4p6a4c/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172652-jt4p6a4c/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172652-jt4p6a4c/media/graph/graph_0_summary_b3c42c99.graph.json\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.668751269578934\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.3951389359103309\n",
            "The running loss is:\n",
            "28.521158926188946\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.0563392194884795\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172652-jt4p6a4c/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172652-jt4p6a4c/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.176970317959785\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.5250729747392513\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172652-jt4p6a4c/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172652-jt4p6a4c/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.591888493858278\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.24414401829104732\n",
            "1\n",
            "The running loss is:\n",
            "7.487370473332703\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.27731001753084084\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172652-jt4p6a4c/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172652-jt4p6a4c/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.26125293970108\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.1948612199889289\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['jt4p6a4c']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.058157247491181\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.22437619435152523\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172652-jt4p6a4c/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172652-jt4p6a4c/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.886977478861809\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.18099916588377069\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172652-jt4p6a4c/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172652-jt4p6a4c/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.049873136915267\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.29814344951538024\n",
            "1\n",
            "The running loss is:\n",
            "6.706313902512193\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.24838199638934047\n",
            "2\n",
            "Data saved to: \n",
            "28_May_202005_26PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172652-jt4p6a4c/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172652-jt4p6a4c/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_26PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
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            "73  2020-04-29  sub_region  ...             73  751.328247\n",
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            "[20 rows x 28 columns]\n"
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          "name": "stdout"
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        {
          "output_type": "stream",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172652-jt4p6a4c/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172652-jt4p6a4c/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172652-jt4p6a4c/wandb-summary.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172652-jt4p6a4c/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172652-jt4p6a4c/media/plotly/test_plot_20_9c0dfd36.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172652-jt4p6a4c/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172652-jt4p6a4c/media/plotly\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: jt4p6a4c \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172652-jt4p6a4c/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172652-jt4p6a4c/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172652-jt4p6a4c/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172652-jt4p6a4c/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172652-jt4p6a4c/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172652-jt4p6a4c/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: jt4p6a4c\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 2gjy2l2o with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 2gjy2l2o\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/2gjy2l2o\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/2gjy2l2o</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/2gjy2l2o\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjJnankybDJvOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172708-2gjy2l2o/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172708-2gjy2l2o/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172708-2gjy2l2o/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172708-2gjy2l2o/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172708-2gjy2l2o/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172708-2gjy2l2o/media/graph/graph_0_summary_53cb4522.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172708-2gjy2l2o/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172708-2gjy2l2o/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172708-2gjy2l2o/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "23.849574618041515\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.883317578445982\n",
            "The running loss is:\n",
            "20.568836465477943\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.7618087579806646\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172708-2gjy2l2o/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172708-2gjy2l2o/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.27038937062025\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7137181248377871\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172708-2gjy2l2o/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172708-2gjy2l2o/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.25364562869072\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.6390239121737303\n",
            "1\n",
            "The running loss is:\n",
            "14.78194186091423\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.5474793281820085\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172708-2gjy2l2o/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172708-2gjy2l2o/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.99204146116972\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.40711264670998965\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['2gjy2l2o']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "34.4778664149344\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "1.2769580153679405\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172708-2gjy2l2o/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172708-2gjy2l2o/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.340780273079872\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.6792881582622174\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172708-2gjy2l2o/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172708-2gjy2l2o/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.76011623442173\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.6948191197933974\n",
            "2\n",
            "The running loss is:\n",
            "18.743604812771082\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.6942075856581882\n",
            "Data saved to: \n",
            "28_May_202005_27PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_27PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172708-2gjy2l2o/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172708-2gjy2l2o/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172708-2gjy2l2o/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  351.287323\n",
            "66  2020-04-22  sub_region  ...             66  351.352722\n",
            "67  2020-04-23  sub_region  ...             67  351.261353\n",
            "68  2020-04-24  sub_region  ...             68  351.227020\n",
            "69  2020-04-25  sub_region  ...             69  351.256317\n",
            "70  2020-04-26  sub_region  ...             70  351.258667\n",
            "71  2020-04-27  sub_region  ...             71  351.388000\n",
            "72  2020-04-28  sub_region  ...             72  351.195374\n",
            "73  2020-04-29  sub_region  ...             73  351.413818\n",
            "74  2020-04-30  sub_region  ...             74  351.286987\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172708-2gjy2l2o/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172708-2gjy2l2o/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172708-2gjy2l2o/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172708-2gjy2l2o/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172708-2gjy2l2o/media/plotly/test_plot_20_3234c249.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172708-2gjy2l2o/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172708-2gjy2l2o/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 2gjy2l2o \n",
            "\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172708-2gjy2l2o/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172708-2gjy2l2o/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172708-2gjy2l2o/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172708-2gjy2l2o/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172708-2gjy2l2o/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 2gjy2l2o\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: ei4en7rs with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: ei4en7rs\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/ei4en7rs\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/ei4en7rs</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/ei4en7rs\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmVpNGVuN3JzOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172726-ei4en7rs/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172726-ei4en7rs/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172726-ei4en7rs/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172726-ei4en7rs/media/graph/graph_0_summary_4e0b46a5.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172726-ei4en7rs/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172726-ei4en7rs/requirements.txt\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172726-ei4en7rs/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "23.785254307091236\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.8809353447070828\n",
            "The running loss is:\n",
            "20.365187123417854\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.7542661897562168\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172726-ei4en7rs/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172726-ei4en7rs/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.407820589840412\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7188081699940894\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172726-ei4en7rs/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172726-ei4en7rs/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.047366067767143\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.594346891398783\n",
            "1\n",
            "The running loss is:\n",
            "16.049381844699383\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.5944215498036809\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172726-ei4en7rs/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172726-ei4en7rs/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.695411691442132\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.3220522848682271\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['ei4en7rs']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.989098764955997\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.4440406949983703\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172726-ei4en7rs/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172726-ei4en7rs/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.975145380944014\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.48056094003496347\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172726-ei4en7rs/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172726-ei4en7rs/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.981494817882776\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.25857388214380655\n",
            "The running loss is:\n",
            "7.491051498800516\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.27744635180742655\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_27PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172726-ei4en7rs/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172726-ei4en7rs/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_27PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  709.696960\n",
            "66  2020-04-22  sub_region  ...             66  709.161499\n",
            "67  2020-04-23  sub_region  ...             67  708.643188\n",
            "68  2020-04-24  sub_region  ...             68  708.597656\n",
            "69  2020-04-25  sub_region  ...             69  708.908020\n",
            "70  2020-04-26  sub_region  ...             70  708.755493\n",
            "71  2020-04-27  sub_region  ...             71  709.836182\n",
            "72  2020-04-28  sub_region  ...             72  708.476990\n",
            "73  2020-04-29  sub_region  ...             73  709.191528\n",
            "74  2020-04-30  sub_region  ...             74  708.709473\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172726-ei4en7rs/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172726-ei4en7rs/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: ei4en7rs \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172726-ei4en7rs/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172726-ei4en7rs/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172726-ei4en7rs/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172726-ei4en7rs/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172726-ei4en7rs/media/plotly/test_plot_20_509b02c0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172726-ei4en7rs/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172726-ei4en7rs/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172726-ei4en7rs/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: ei4en7rs\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 2ahqajh0 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 2ahqajh0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/2ahqajh0\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/2ahqajh0</a><br/>\n",
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          "metadata": {
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        {
          "output_type": "stream",
          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/2ahqajh0\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjJhaHFhamgwOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172742-2ahqajh0/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172742-2ahqajh0/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172742-2ahqajh0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172742-2ahqajh0/media/graph/graph_0_summary_28353f47.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172742-2ahqajh0/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172742-2ahqajh0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172742-2ahqajh0/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172742-2ahqajh0/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172742-2ahqajh0/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "25.31017677485943\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.9734683374945934\n",
            "The running loss is:\n",
            "17.691114902496338\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.6804274962498591\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172742-2ahqajh0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172742-2ahqajh0/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.284134477376938\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.8186205568221899\n",
            "The running loss is:\n",
            "14.811767756938934\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.5696833752668821\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172742-2ahqajh0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172742-2ahqajh0/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.015214286744595\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.46212362641325366\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172742-2ahqajh0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172742-2ahqajh0/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.353260762989521\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.3982023370380585\n",
            "2\n",
            "The running loss is:\n",
            "7.667264841496944\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.2948948015960363\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_27PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.wandb_agent:Running runs: ['2ahqajh0']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172742-2ahqajh0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172742-2ahqajh0/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_27PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  318.850616\n",
            "66  2020-04-22  sub_region  ...             66  316.706177\n",
            "67  2020-04-23  sub_region  ...             67  320.013977\n",
            "68  2020-04-24  sub_region  ...             68  321.565765\n",
            "69  2020-04-25  sub_region  ...             69  321.352051\n",
            "70  2020-04-26  sub_region  ...             70  320.382446\n",
            "71  2020-04-27  sub_region  ...             71  318.602081\n",
            "72  2020-04-28  sub_region  ...             72  318.317017\n",
            "73  2020-04-29  sub_region  ...             73  318.346680\n",
            "74  2020-04-30  sub_region  ...             74  320.484741\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172742-2ahqajh0/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172742-2ahqajh0/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 2ahqajh0 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172742-2ahqajh0/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172742-2ahqajh0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172742-2ahqajh0/media/plotly/test_plot_14_b1c54b64.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172742-2ahqajh0/media/plotly/test_plot_all_15_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172742-2ahqajh0/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172742-2ahqajh0/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172742-2ahqajh0/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 2ahqajh0\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 9cofme0n with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 9cofme0n\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/9cofme0n\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/9cofme0n</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/9cofme0n\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjljb2ZtZTBuOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/wandb-events.jsonl\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/wandb-summary.json\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "25.090075597167015\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.9650029075833467\n",
            "The running loss is:\n",
            "17.45224541425705\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.6712402082406558\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.34785506129265\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.8210713485112557\n",
            "The running loss is:\n",
            "14.617094554007053\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.5621959443848866\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "12.857044279575348\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.4945017030605903\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.908861950039864\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.4195716134630717\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['9cofme0n']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.671748608350754\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.2950672541673367\n",
            "The running loss is:\n",
            "6.437005430459976\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.2475771319407683\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.865247845649719\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.3025095325249892\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.2042678743600845\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.2001641490138494\n",
            "Data saved to: \n",
            "28_May_202005_28PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_28PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/config.yaml\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  570.512756\n",
            "66  2020-04-22  sub_region  ...             66  571.143372\n",
            "67  2020-04-23  sub_region  ...             67  570.903015\n",
            "68  2020-04-24  sub_region  ...             68  570.691162\n",
            "69  2020-04-25  sub_region  ...             69  570.595093\n",
            "70  2020-04-26  sub_region  ...             70  570.591003\n",
            "71  2020-04-27  sub_region  ...             71  570.901611\n",
            "72  2020-04-28  sub_region  ...             72  570.663696\n",
            "73  2020-04-29  sub_region  ...             73  571.259827\n",
            "74  2020-04-30  sub_region  ...             74  571.026001\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/media/plotly/test_plot_20_96e49c04.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 9cofme0n \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172758-9cofme0n/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 9cofme0n\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: dg2wr63w with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: dg2wr63w\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/dg2wr63w\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/dg2wr63w</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/dg2wr63w\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmRnMndyNjN3OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172814-dg2wr63w/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172814-dg2wr63w/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172814-dg2wr63w/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172814-dg2wr63w/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172814-dg2wr63w/media/graph/graph_0_summary_16b3fa38.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172814-dg2wr63w/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172814-dg2wr63w/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172814-dg2wr63w/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172814-dg2wr63w/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "24.998789221048355\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.9614918931172445\n",
            "The running loss is:\n",
            "19.62556444108486\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.754829401580187\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172814-dg2wr63w/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172814-dg2wr63w/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.021592140197754\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.7315996976999136\n",
            "The running loss is:\n",
            "13.53062754869461\n",
            "The number of items in train is: \n",
            "26\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172814-dg2wr63w/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The loss for epoch 3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172814-dg2wr63w/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0.5204087518728696\n",
            "The running loss is:\n",
            "11.939093425869942\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.45919590099499774\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172814-dg2wr63w/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172814-dg2wr63w/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.370361641049385\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.475783140040361\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['dg2wr63w']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.105389013886452\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.388668808226402\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172814-dg2wr63w/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172814-dg2wr63w/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.374523870646954\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.20671245656334436\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172814-dg2wr63w/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172814-dg2wr63w/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.875691753812134\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.18752660591585132\n",
            "The running loss is:\n",
            "7.123469600221142\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.27397960000850546\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_28PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172814-dg2wr63w/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172814-dg2wr63w/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_28PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  697.734924\n",
            "66  2020-04-22  sub_region  ...             66  699.246887\n",
            "67  2020-04-23  sub_region  ...             67  698.447754\n",
            "68  2020-04-24  sub_region  ...             68  697.592102\n",
            "69  2020-04-25  sub_region  ...             69  697.349731\n",
            "70  2020-04-26  sub_region  ...             70  697.215332\n",
            "71  2020-04-27  sub_region  ...             71  698.537048\n",
            "72  2020-04-28  sub_region  ...             72  697.177002\n",
            "73  2020-04-29  sub_region  ...             73  699.459961\n",
            "74  2020-04-30  sub_region  ...             74  698.878723\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172814-dg2wr63w/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172814-dg2wr63w/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172814-dg2wr63w/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172814-dg2wr63w/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172814-dg2wr63w/media/plotly/test_plot_20_70b23860.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172814-dg2wr63w/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172814-dg2wr63w/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: dg2wr63w \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172814-dg2wr63w/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172814-dg2wr63w/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172814-dg2wr63w/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172814-dg2wr63w/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172814-dg2wr63w/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172814-dg2wr63w/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: dg2wr63w\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: okra1ju6 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: okra1ju6\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/okra1ju6\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/okra1ju6</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/okra1ju6\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOm9rcmExanU2OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172833-okra1ju6/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172833-okra1ju6/media/graph/graph_0_summary_50893349.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172833-okra1ju6/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172833-okra1ju6/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172833-okra1ju6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172833-okra1ju6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172833-okra1ju6/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172833-okra1ju6/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172833-okra1ju6/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "24.827137678861618\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.9548899107254468\n",
            "The running loss is:\n",
            "19.719864428043365\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.758456324155514\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172833-okra1ju6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172833-okra1ju6/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.63474379479885\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.7167209151845712\n",
            "The running loss is:\n",
            "13.806637480854988\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.5310245184944227\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172833-okra1ju6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172833-okra1ju6/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "11.331828974187374\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.4358395759302836\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172833-okra1ju6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172833-okra1ju6/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.775343157351017\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.4144362752827314\n",
            "1\n",
            "The running loss is:\n",
            "10.32153557986021\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.3969821376869312\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['okra1ju6']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172833-okra1ju6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172833-okra1ju6/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.037654765415937\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.2706790294390745\n",
            "The running loss is:\n",
            "14.718151992186904\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.5660827689302655\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172833-okra1ju6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172833-okra1ju6/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.737017912324518\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.22065453508940452\n",
            "Data saved to: \n",
            "28_May_202005_28PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_28PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172833-okra1ju6/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172833-okra1ju6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172833-okra1ju6/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  632.474121\n",
            "66  2020-04-22  sub_region  ...             66  634.329102\n",
            "67  2020-04-23  sub_region  ...             67  632.773071\n",
            "68  2020-04-24  sub_region  ...             68  631.713074\n",
            "69  2020-04-25  sub_region  ...             69  631.196228\n",
            "70  2020-04-26  sub_region  ...             70  631.151917\n",
            "71  2020-04-27  sub_region  ...             71  632.160889\n",
            "72  2020-04-28  sub_region  ...             72  631.541016\n",
            "73  2020-04-29  sub_region  ...             73  633.977478\n",
            "74  2020-04-30  sub_region  ...             74  632.605713\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172833-okra1ju6/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172833-okra1ju6/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172833-okra1ju6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172833-okra1ju6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172833-okra1ju6/media/plotly/test_plot_20_a4b0535b.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172833-okra1ju6/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172833-okra1ju6/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: okra1ju6 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172833-okra1ju6/wandb-events.jsonl\n",
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            "INFO:wandb.wandb_agent:Cleaning up finished run: okra1ju6\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
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            "\tlr: 0.0001\n",
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            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: sgw7c1ep\n"
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            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/sgw7c1ep\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/sgw7c1ep</a><br/>\n",
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          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
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        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172848-sgw7c1ep/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172848-sgw7c1ep/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172848-sgw7c1ep/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172848-sgw7c1ep/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172848-sgw7c1ep/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172848-sgw7c1ep/media/graph/graph_0_summary_3ce5022b.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172848-sgw7c1ep/requirements.txt\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.056726850569248\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 0\n",
            "0.5734545303774732\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172848-sgw7c1ep/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172848-sgw7c1ep/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "46.208357490599155\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 1\n",
            "1.6502984818071127\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172848-sgw7c1ep/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172848-sgw7c1ep/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "29.032308392226696\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 2\n",
            "1.0368681568652391\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172848-sgw7c1ep/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172848-sgw7c1ep/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "28.841471418738365\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 3\n",
            "1.0300525506692273\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['sgw7c1ep']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172848-sgw7c1ep/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172848-sgw7c1ep/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "28.338371301069856\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 4\n",
            "1.0120846893239235\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172848-sgw7c1ep/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172848-sgw7c1ep/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "28.69572694809176\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 5\n",
            "1.0248473910032772\n",
            "2\n",
            "The running loss is:\n",
            "19.646056853234768\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 6\n",
            "0.7016448876155275\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172848-sgw7c1ep/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172848-sgw7c1ep/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_28PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_28PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172848-sgw7c1ep/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
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          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172848-sgw7c1ep/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172848-sgw7c1ep/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172848-sgw7c1ep/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172848-sgw7c1ep/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172848-sgw7c1ep/media/plotly/test_plot_14_bdfadf79.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172848-sgw7c1ep/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172848-sgw7c1ep/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: sgw7c1ep \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172848-sgw7c1ep/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172848-sgw7c1ep/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172848-sgw7c1ep/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172848-sgw7c1ep/media/plotly/test_plot_all_15_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172848-sgw7c1ep/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172848-sgw7c1ep/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: sgw7c1ep\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: xptik3ho with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: xptik3ho\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/xptik3ho\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/xptik3ho</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/xptik3ho\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnhwdGlrM2hvOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172904-xptik3ho/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172904-xptik3ho/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172904-xptik3ho/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172904-xptik3ho/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172904-xptik3ho/media/graph/graph_0_summary_c719c3e7.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172904-xptik3ho/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172904-xptik3ho/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172904-xptik3ho/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172904-xptik3ho/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.879746047779918\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 0\n",
            "0.4957052159921399\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172904-xptik3ho/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172904-xptik3ho/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "50.16468261182308\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 1\n",
            "1.7915958075651102\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172904-xptik3ho/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172904-xptik3ho/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "28.752860818058252\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 2\n",
            "1.0268878863592232\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172904-xptik3ho/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172904-xptik3ho/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "28.75844579562545\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 3\n",
            "1.027087349843766\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['xptik3ho']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172904-xptik3ho/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172904-xptik3ho/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "28.228818399831653\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 4\n",
            "1.0081720857082732\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172904-xptik3ho/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172904-xptik3ho/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "28.275303000351414\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 5\n",
            "1.0098322500125505\n",
            "2\n",
            "The running loss is:\n",
            "19.328091056086123\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 6\n",
            "0.6902889662887901\n",
            "3\n",
            "Stopping model now\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172904-xptik3ho/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172904-xptik3ho/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_29PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_29PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  429.017181\n",
            "66  2020-04-22  sub_region  ...             66  428.986389\n",
            "67  2020-04-23  sub_region  ...             67  429.089111\n",
            "68  2020-04-24  sub_region  ...             68  429.224731\n",
            "69  2020-04-25  sub_region  ...             69  429.164246\n",
            "70  2020-04-26  sub_region  ...             70  429.191895\n",
            "71  2020-04-27  sub_region  ...             71  428.905151\n",
            "72  2020-04-28  sub_region  ...             72  429.181915\n",
            "73  2020-04-29  sub_region  ...             73  428.645477\n",
            "74  2020-04-30  sub_region  ...             74  428.955353\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172904-xptik3ho/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172904-xptik3ho/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172904-xptik3ho/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172904-xptik3ho/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172904-xptik3ho/media/plotly/test_plot_14_562144b7.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172904-xptik3ho/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172904-xptik3ho/media/plotly\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: xptik3ho \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172904-xptik3ho/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172904-xptik3ho/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172904-xptik3ho/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172904-xptik3ho/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172904-xptik3ho/media/plotly/test_plot_all_15_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172904-xptik3ho/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: xptik3ho\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: rhkbw3nt with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: rhkbw3nt\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/rhkbw3nt\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/rhkbw3nt</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/rhkbw3nt\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnJoa2J3M250OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/media/graph/graph_0_summary_a092fa95.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.515796359628439\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.3524369022084607\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "31.958917625248432\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.1836636157499418\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.400996170938015\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.6444813396643709\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.490230657160282\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.647786320635566\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['rhkbw3nt']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.88597560673952\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.6624435409903526\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.275350011885166\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.6398277782179691\n",
            "The running loss is:\n",
            "17.80729231238365\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.6595293449030982\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.339848961681128\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.6422166282104121\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.749763071537018\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.6203615952421118\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.160026656463742\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.6355565428319905\n",
            "Data saved to: \n",
            "28_May_202005_29PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_29PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  452.964355\n",
            "66  2020-04-22  sub_region  ...             66  448.373505\n",
            "67  2020-04-23  sub_region  ...             67  451.731903\n",
            "68  2020-04-24  sub_region  ...             68  454.205078\n",
            "69  2020-04-25  sub_region  ...             69  454.852173\n",
            "70  2020-04-26  sub_region  ...             70  454.945068\n",
            "71  2020-04-27  sub_region  ...             71  451.557831\n",
            "72  2020-04-28  sub_region  ...             72  453.470245\n",
            "73  2020-04-29  sub_region  ...             73  448.514465\n",
            "74  2020-04-30  sub_region  ...             74  451.503876\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/media/plotly/test_plot_20_da2b038c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/media/plotly\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: rhkbw3nt \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172920-rhkbw3nt/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: rhkbw3nt\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: vt6j1jur with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: vt6j1jur\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/vt6j1jur\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/vt6j1jur</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/vt6j1jur\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnZ0NmoxanVyOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/media/graph/graph_0_summary_d7fd0963.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.540501032024622\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.353351890074986\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "32.01947431452572\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.1859064560935453\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.433912321925163\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.6457004563675987\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.372653737664223\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.6434316199134897\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['vt6j1jur']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.937954049557447\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.6643686685021277\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.54516338557005\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.649820866132224\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.718922704458237\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.6562563964614162\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.30660080164671\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.6409852148758041\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.718926943838596\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.6192195164384665\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.827449632808566\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.6232388752892062\n",
            "Data saved to: \n",
            "28_May_202005_29PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_29PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  457.992615\n",
            "66  2020-04-22  sub_region  ...             66  454.040710\n",
            "67  2020-04-23  sub_region  ...             67  456.812286\n",
            "68  2020-04-24  sub_region  ...             68  459.140167\n",
            "69  2020-04-25  sub_region  ...             69  459.434723\n",
            "70  2020-04-26  sub_region  ...             70  459.551544\n",
            "71  2020-04-27  sub_region  ...             71  456.045074\n",
            "72  2020-04-28  sub_region  ...             72  458.655548\n",
            "73  2020-04-29  sub_region  ...             73  453.452576\n",
            "74  2020-04-30  sub_region  ...             74  456.783661\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: vt6j1jur \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/media/plotly/test_plot_20_c566c54f.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172941-vt6j1jur/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: vt6j1jur\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: j1akj0pq with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: j1akj0pq\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/j1akj0pq\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/j1akj0pq</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/j1akj0pq\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmoxYWtqMHBxOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/media/graph/graph_0_summary_2890e43d.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "31.62208504229784\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "1.17118833489992\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.408665716648102\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.7188394709869668\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.407186299562454\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7558217147986094\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.506580412387848\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.759502978236587\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['j1akj0pq']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.30820331722498\n",
            "The number of items in train is: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "27\n",
            "The loss for epoch 4\n",
            "0.7521556784157399\n",
            "The running loss is:\n",
            "19.309663198888302\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.7151727110699371\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.341759957373142\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.679324442865672\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.737579837441444\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.6569474013867201\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.28192499279976\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.7141453701036947\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.758581429719925\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.694762275174812\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_30PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_30PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  337.341278\n",
            "66  2020-04-22  sub_region  ...             66  337.345581\n",
            "67  2020-04-23  sub_region  ...             67  337.355377\n",
            "68  2020-04-24  sub_region  ...             68  337.360901\n",
            "69  2020-04-25  sub_region  ...             69  337.360565\n",
            "70  2020-04-26  sub_region  ...             70  337.355988\n",
            "71  2020-04-27  sub_region  ...             71  337.354065\n",
            "72  2020-04-28  sub_region  ...             72  337.343140\n",
            "73  2020-04-29  sub_region  ...             73  337.349548\n",
            "74  2020-04-30  sub_region  ...             74  337.355652\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/wandb-summary.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/media/plotly/test_plot_20_e91edfe1.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
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          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: j1akj0pq \n",
            "\n"
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_172957-j1akj0pq/media/plotly\n",
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            "INFO:wandb.wandb_agent:Cleaning up finished run: j1akj0pq\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
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          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 30ix45tz with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 30ix45tz\n"
          ],
          "name": "stdout"
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          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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          "name": "stderr"
        },
        {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/30ix45tz\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/30ix45tz</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/30ix45tz\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjMwaXg0NXR6OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
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            "INFO:wandb.run_manager:saving pip packages\n",
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        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
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        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/media/graph/graph_0_summary_a11268f2.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/wandb-summary.json\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "31.686953715980053\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "1.1735908783696316\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.326208144426346\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.7157854868306054\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.44809077680111\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7573366954370782\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.70396327972412\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.7668134548045971\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['30ix45tz']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.114036343991756\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.7449643090367317\n",
            "1\n",
            "The running loss is:\n",
            "19.89861784130335\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.7369858459741981\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.967432893812656\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.7024975145856539\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.17745415121317\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.7102760796745619\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.25604397803545\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.713186814001313\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.80481818318367\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.6964747475253211\n",
            "Data saved to: \n",
            "28_May_202005_30PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_30PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  353.958618\n",
            "66  2020-04-22  sub_region  ...             66  353.994934\n",
            "67  2020-04-23  sub_region  ...             67  353.966064\n",
            "68  2020-04-24  sub_region  ...             68  353.948608\n",
            "69  2020-04-25  sub_region  ...             69  353.950348\n",
            "70  2020-04-26  sub_region  ...             70  353.951172\n",
            "71  2020-04-27  sub_region  ...             71  353.985535\n",
            "72  2020-04-28  sub_region  ...             72  353.945618\n",
            "73  2020-04-29  sub_region  ...             73  354.007202\n",
            "74  2020-04-30  sub_region  ...             74  353.972107\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/media/plotly/test_plot_20_384740d4.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 30ix45tz \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173018-30ix45tz/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 30ix45tz\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 8r2cy0dd with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 8r2cy0dd\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/8r2cy0dd\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/8r2cy0dd</a><br/>\n",
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        {
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/8r2cy0dd\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjhyMmN5MGRkOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/media/graph/graph_0_summary_6452539b.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/wandb-history.jsonl\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "28.677166178822517\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "1.1029679299547122\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.185990750789642\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.737922721184217\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.213715583086014\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.777450599349462\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.579263865947723\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.5992024563826047\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['8r2cy0dd']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.614615187048912\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.6005621225788043\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.192182525992393\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.5458531740766305\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.893233135342598\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.6882012744362538\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.887526050209999\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.6110586942388461\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.056910991668701\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.50218888429495\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.211280450224876\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.39274155577787984\n",
            "2\n",
            "Data saved to: \n",
            "28_May_202005_30PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_30PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  409.355835\n",
            "66  2020-04-22  sub_region  ...             66  409.509674\n",
            "67  2020-04-23  sub_region  ...             67  409.471558\n",
            "68  2020-04-24  sub_region  ...             68  409.399261\n",
            "69  2020-04-25  sub_region  ...             69  409.354004\n",
            "70  2020-04-26  sub_region  ...             70  409.365173\n",
            "71  2020-04-27  sub_region  ...             71  409.424011\n",
            "72  2020-04-28  sub_region  ...             72  409.376404\n",
            "73  2020-04-29  sub_region  ...             73  409.552612\n",
            "74  2020-04-30  sub_region  ...             74  409.473022\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/media/plotly/test_plot_20_eefb9a0f.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 8r2cy0dd \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173035-8r2cy0dd/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 8r2cy0dd\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 6yrex3hf with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 6yrex3hf\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/6yrex3hf\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/6yrex3hf</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/6yrex3hf\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjZ5cmV4M2hmOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/media/graph/graph_0_summary_13008330.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "28.61015349626541\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "1.1003905190871313\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.129196166992188\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.7357383141150842\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.223093792796135\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.7778112997229283\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.592137679457664\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.599697603056064\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['6yrex3hf']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.741975575685501\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.5669990606032885\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.141681730747223\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.5439108357979701\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.65192748606205\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.6789202879254634\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.934842199087143\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.5359554691956594\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.86246071755886\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.4177869506753408\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.102634258568287\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.3501013176372418\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_31PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_31PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/config.yaml\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  475.105896\n",
            "66  2020-04-22  sub_region  ...             66  475.061249\n",
            "67  2020-04-23  sub_region  ...             67  475.085632\n",
            "68  2020-04-24  sub_region  ...             68  475.112213\n",
            "69  2020-04-25  sub_region  ...             69  475.114807\n",
            "70  2020-04-26  sub_region  ...             70  475.139618\n",
            "71  2020-04-27  sub_region  ...             71  475.079346\n",
            "72  2020-04-28  sub_region  ...             72  475.173126\n",
            "73  2020-04-29  sub_region  ...             73  475.043427\n",
            "74  2020-04-30  sub_region  ...             74  475.085358\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/wandb-history.jsonl\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173056-6yrex3hf/media/plotly\n",
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            "INFO:wandb.wandb_agent:Cleaning up finished run: 6yrex3hf\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
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            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
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            "wandb: Agent Starting Run: zi373v86 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: zi373v86\n"
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          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/zi373v86\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/zi373v86</a><br/>\n",
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          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
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          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173117-zi373v86/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173117-zi373v86/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173117-zi373v86/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173117-zi373v86/media/graph/graph_0_summary_cfdce5d6.graph.json\n",
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          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "26.287698671221733\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "1.0110653335085282\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173117-zi373v86/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173117-zi373v86/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.019511476159096\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.8469042875445806\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173117-zi373v86/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173117-zi373v86/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.7527959048748\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.7597229194182616\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173117-zi373v86/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173117-zi373v86/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.987582370638847\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.6149070142553403\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['zi373v86']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.31729331612587\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.5891266660048411\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173117-zi373v86/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173117-zi373v86/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.650041669607162\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.5250016026771985\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173117-zi373v86/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173117-zi373v86/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.348268389701843\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.5133949380654556\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173117-zi373v86/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173117-zi373v86/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.254892647266388\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.4328804864333226\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_31PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_31PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173117-zi373v86/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173117-zi373v86/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173117-zi373v86/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  347.929901\n",
            "66  2020-04-22  sub_region  ...             66  348.376862\n",
            "67  2020-04-23  sub_region  ...             67  348.171692\n",
            "68  2020-04-24  sub_region  ...             68  347.880554\n",
            "69  2020-04-25  sub_region  ...             69  347.761993\n",
            "70  2020-04-26  sub_region  ...             70  347.774658\n",
            "71  2020-04-27  sub_region  ...             71  348.046265\n",
            "72  2020-04-28  sub_region  ...             72  347.813934\n",
            "73  2020-04-29  sub_region  ...             73  348.664154\n",
            "74  2020-04-30  sub_region  ...             74  348.247253\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173117-zi373v86/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173117-zi373v86/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173117-zi373v86/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173117-zi373v86/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173117-zi373v86/media/plotly/test_plot_16_3f181331.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173117-zi373v86/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173117-zi373v86/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: zi373v86 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173117-zi373v86/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173117-zi373v86/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173117-zi373v86/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173117-zi373v86/media/plotly/test_plot_all_17_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173117-zi373v86/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173117-zi373v86/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: zi373v86\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: ock9vc16 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: ock9vc16\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/ock9vc16\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/ock9vc16</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/ock9vc16\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOm9jazl2YzE2OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/media/graph/graph_0_summary_7dd8cccf.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "26.332314282655716\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "1.0127813185636814\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.822422578930855\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.8393239453434944\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.969776809215546\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.7680683388159826\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.921830900013447\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.6123781115389787\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['ock9vc16']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.494909837841988\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.5959580706862303\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.208502128720284\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.5464808511046263\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.151983082294464\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.621230118549787\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.41115165501833\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.4773519867314742\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.447976488620043\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.3633837111007709\n",
            "The running loss is:\n",
            "9.989659074693918\n",
            "The number of items in train is: \n",
            "26\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The loss for epoch 9\n",
            "0.3842176567189969\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "Data saved to: \n",
            "28_May_202005_31PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_31PM_model.pth\n",
            "interpolate should be below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  578.508301\n",
            "66  2020-04-22  sub_region  ...             66  578.518188\n",
            "67  2020-04-23  sub_region  ...             67  578.516479\n",
            "68  2020-04-24  sub_region  ...             68  578.510986\n",
            "69  2020-04-25  sub_region  ...             69  578.503906\n",
            "70  2020-04-26  sub_region  ...             70  578.503906\n",
            "71  2020-04-27  sub_region  ...             71  578.500427\n",
            "72  2020-04-28  sub_region  ...             72  578.506470\n",
            "73  2020-04-29  sub_region  ...             73  578.516052\n",
            "74  2020-04-30  sub_region  ...             74  578.512817\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/media/plotly/test_plot_20_ae1f0e4f.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/media\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: ock9vc16 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173133-ock9vc16/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: ock9vc16\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: h21b0f2d with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: h21b0f2d\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/h21b0f2d\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/h21b0f2d</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/h21b0f2d\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmgyMWIwZjJkOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "15.545928395004012\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 0\n",
            "0.5552117283930004\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173154-h21b0f2d/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173154-h21b0f2d/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173154-h21b0f2d/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173154-h21b0f2d/media/graph/graph_0_summary_d7c91db2.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173154-h21b0f2d/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173154-h21b0f2d/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173154-h21b0f2d/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173154-h21b0f2d/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173154-h21b0f2d/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.661523299873807\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 1\n",
            "0.7736258321383502\n",
            "The running loss is:\n",
            "14.07579830638133\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 2\n",
            "0.5027070823707618\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173154-h21b0f2d/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173154-h21b0f2d/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.572435699170455\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 3\n",
            "0.4133012749703734\n",
            "1\n",
            "The running loss is:\n",
            "11.783770642941818\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 4\n",
            "0.42084895153363633\n",
            "The running loss is:\n",
            "9.61011728970334\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 5\n",
            "0.3432184746322621\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173154-h21b0f2d/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173154-h21b0f2d/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.171849727863446\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 6\n",
            "0.4704232045665516\n",
            "The running loss is:\n",
            "9.562567624612711\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 7\n",
            "0.34152027230759685\n",
            "1\n",
            "The running loss is:\n",
            "11.004484111152124\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 8\n",
            "0.3930172896840044\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173154-h21b0f2d/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173154-h21b0f2d/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "2\n",
            "The running loss is:\n",
            "10.858391232584836\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 9\n",
            "0.38779968687802985\n",
            "Data saved to: \n",
            "28_May_202005_31PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.wandb_agent:Running runs: ['h21b0f2d']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_31PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173154-h21b0f2d/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173154-h21b0f2d/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173154-h21b0f2d/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  856.106934\n",
            "66  2020-04-22  sub_region  ...             66  898.349731\n",
            "67  2020-04-23  sub_region  ...             67  857.475891\n",
            "68  2020-04-24  sub_region  ...             68  861.468872\n",
            "69  2020-04-25  sub_region  ...             69  885.428162\n",
            "70  2020-04-26  sub_region  ...             70  897.812500\n",
            "71  2020-04-27  sub_region  ...             71  894.293274\n",
            "72  2020-04-28  sub_region  ...             72  929.067383\n",
            "73  2020-04-29  sub_region  ...             73  878.263550\n",
            "74  2020-04-30  sub_region  ...             74  869.549072\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173154-h21b0f2d/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173154-h21b0f2d/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173154-h21b0f2d/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173154-h21b0f2d/wandb-history.jsonl\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173154-h21b0f2d/media/plotly/test_plot_20_314de4b9.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173154-h21b0f2d/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173154-h21b0f2d/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: h21b0f2d \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173154-h21b0f2d/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173154-h21b0f2d/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173154-h21b0f2d/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173154-h21b0f2d/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173154-h21b0f2d/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173154-h21b0f2d/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: h21b0f2d\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: j5xzumca with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: j5xzumca\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/j5xzumca\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/j5xzumca</a><br/>\n",
              "            "
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            "DEBUG:wandb.meta:probe for git information\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/j5xzumca\n",
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            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmo1eHp1bWNhOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "15.36638443195261\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 0\n",
            "0.5487994439983075\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173210-j5xzumca/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173210-j5xzumca/media/graph/graph_0_summary_751e4c9c.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173210-j5xzumca/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173210-j5xzumca/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173210-j5xzumca/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173210-j5xzumca/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173210-j5xzumca/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173210-j5xzumca/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173210-j5xzumca/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.380546062253416\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 1\n",
            "0.6921623593661934\n",
            "The running loss is:\n",
            "13.36081693135202\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 2\n",
            "0.4771720332625721\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173210-j5xzumca/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173210-j5xzumca/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.953934513032436\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 3\n",
            "0.4269262326083013\n",
            "1\n",
            "The running loss is:\n",
            "12.163511937658768\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 4\n",
            "0.43441114063067027\n",
            "The running loss is:\n",
            "9.378338906506542\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 5\n",
            "0.33494067523237653\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173210-j5xzumca/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173210-j5xzumca/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.704894061491359\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 6\n",
            "0.4537462164818343\n",
            "The running loss is:\n",
            "11.437989893951453\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 7\n",
            "0.4084996390696948\n",
            "1\n",
            "The running loss is:\n",
            "11.367679933318868\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 8\n",
            "0.40598856904710245\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173210-j5xzumca/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173210-j5xzumca/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.42585400538519\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 9\n",
            "0.40806621447804253\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_32PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_32PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['j5xzumca']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173210-j5xzumca/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173210-j5xzumca/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173210-j5xzumca/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  802.753906\n",
            "66  2020-04-22  sub_region  ...             66  877.084839\n",
            "67  2020-04-23  sub_region  ...             67  830.681274\n",
            "68  2020-04-24  sub_region  ...             68  816.030273\n",
            "69  2020-04-25  sub_region  ...             69  821.921509\n",
            "70  2020-04-26  sub_region  ...             70  826.182373\n",
            "71  2020-04-27  sub_region  ...             71  846.830322\n",
            "72  2020-04-28  sub_region  ...             72  856.710266\n",
            "73  2020-04-29  sub_region  ...             73  857.412720\n",
            "74  2020-04-30  sub_region  ...             74  841.012085\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173210-j5xzumca/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173210-j5xzumca/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173210-j5xzumca/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173210-j5xzumca/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173210-j5xzumca/media/plotly/test_plot_20_e30096eb.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173210-j5xzumca/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173210-j5xzumca/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: j5xzumca \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173210-j5xzumca/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173210-j5xzumca/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173210-j5xzumca/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173210-j5xzumca/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173210-j5xzumca/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173210-j5xzumca/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: j5xzumca\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: hhsbtt8o with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: hhsbtt8o\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/hhsbtt8o\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/hhsbtt8o</a><br/>\n",
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          "metadata": {
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        {
          "output_type": "stream",
          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/hhsbtt8o\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmhoc2J0dDhvOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "10.473457891494036\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.38790584783311244\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173221-hhsbtt8o/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173221-hhsbtt8o/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173221-hhsbtt8o/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173221-hhsbtt8o/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173221-hhsbtt8o/media/graph/graph_0_summary_bd7e2cbc.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173221-hhsbtt8o/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173221-hhsbtt8o/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173221-hhsbtt8o/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173221-hhsbtt8o/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.50141921825707\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.4630155266021137\n",
            "1\n",
            "The running loss is:\n",
            "12.783501834142953\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.47346303089418346\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173221-hhsbtt8o/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173221-hhsbtt8o/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.422599470242858\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.20083701741640214\n",
            "1\n",
            "The running loss is:\n",
            "5.399664411786944\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.19998757080692384\n",
            "The running loss is:\n",
            "4.413807349279523\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.16347434626961196\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173221-hhsbtt8o/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173221-hhsbtt8o/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.564219214487821\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.16904515609214152\n",
            "The running loss is:\n",
            "4.552914118394256\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.16862644882941688\n",
            "1\n",
            "The running loss is:\n",
            "4.2418790506199\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.15710663150444074\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173221-hhsbtt8o/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173221-hhsbtt8o/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.331287162844092\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.16041804306829968\n",
            "Data saved to: \n",
            "28_May_202005_32PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_32PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['hhsbtt8o']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173221-hhsbtt8o/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173221-hhsbtt8o/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173221-hhsbtt8o/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  839.469238\n",
            "66  2020-04-22  sub_region  ...             66  751.845520\n",
            "67  2020-04-23  sub_region  ...             67  796.254761\n",
            "68  2020-04-24  sub_region  ...             68  840.047058\n",
            "69  2020-04-25  sub_region  ...             69  856.757812\n",
            "70  2020-04-26  sub_region  ...             70  857.516724\n",
            "71  2020-04-27  sub_region  ...             71  805.222778\n",
            "72  2020-04-28  sub_region  ...             72  846.005188\n",
            "73  2020-04-29  sub_region  ...             73  746.322815\n",
            "74  2020-04-30  sub_region  ...             74  799.226501\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173221-hhsbtt8o/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173221-hhsbtt8o/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173221-hhsbtt8o/wandb-summary.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173221-hhsbtt8o/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173221-hhsbtt8o/media/plotly/test_plot_20_908a465b.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173221-hhsbtt8o/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173221-hhsbtt8o/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: hhsbtt8o \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173221-hhsbtt8o/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173221-hhsbtt8o/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173221-hhsbtt8o/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173221-hhsbtt8o/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173221-hhsbtt8o/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173221-hhsbtt8o/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: hhsbtt8o\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
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            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
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            "wandb: Agent Starting Run: 1pjcq596 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 1pjcq596\n"
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          "name": "stdout"
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          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/1pjcq596\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/1pjcq596</a><br/>\n",
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          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "10.56319503299892\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.39122944566662665\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173237-1pjcq596/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173237-1pjcq596/media/graph/graph_0_summary_20b617bc.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173237-1pjcq596/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173237-1pjcq596/requirements.txt\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173237-1pjcq596/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173237-1pjcq596/wandb-summary.json\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.365171766839921\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.45796932469777485\n",
            "1\n",
            "The running loss is:\n",
            "10.063402196858078\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.37271859988363254\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173237-1pjcq596/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173237-1pjcq596/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.191733076237142\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.19228641023100526\n",
            "1\n",
            "The running loss is:\n",
            "5.048446481116116\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.1869794993005969\n",
            "The running loss is:\n",
            "4.6334264650940895\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.17160838759607738\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173237-1pjcq596/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173237-1pjcq596/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.809368921909481\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.17812477488553635\n",
            "The running loss is:\n",
            "4.469904407858849\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.16555201510588327\n",
            "1\n",
            "The running loss is:\n",
            "4.4312631585635245\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.164120857724575\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173237-1pjcq596/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173237-1pjcq596/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.365964470198378\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.1617023877851251\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_32PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_32PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['1pjcq596']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173237-1pjcq596/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173237-1pjcq596/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173237-1pjcq596/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  820.661011\n",
            "66  2020-04-22  sub_region  ...             66  731.493591\n",
            "67  2020-04-23  sub_region  ...             67  777.978394\n",
            "68  2020-04-24  sub_region  ...             68  817.230530\n",
            "69  2020-04-25  sub_region  ...             69  833.734131\n",
            "70  2020-04-26  sub_region  ...             70  830.960205\n",
            "71  2020-04-27  sub_region  ...             71  781.981934\n",
            "72  2020-04-28  sub_region  ...             72  821.894836\n",
            "73  2020-04-29  sub_region  ...             73  732.720215\n",
            "74  2020-04-30  sub_region  ...             74  782.010132\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173237-1pjcq596/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173237-1pjcq596/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173237-1pjcq596/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173237-1pjcq596/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173237-1pjcq596/media/plotly/test_plot_20_a6760719.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173237-1pjcq596/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173237-1pjcq596/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 1pjcq596 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173237-1pjcq596/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173237-1pjcq596/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173237-1pjcq596/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173237-1pjcq596/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173237-1pjcq596/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173237-1pjcq596/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 1pjcq596\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: pwxzkbkd with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: pwxzkbkd\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/pwxzkbkd\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/pwxzkbkd</a><br/>\n",
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        {
          "output_type": "stream",
          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/pwxzkbkd\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnB3eHprYmtkOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "22.41447990387678\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.8301659223658068\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173253-pwxzkbkd/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173253-pwxzkbkd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173253-pwxzkbkd/media/graph/graph_0_summary_4da48f4f.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173253-pwxzkbkd/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173253-pwxzkbkd/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173253-pwxzkbkd/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173253-pwxzkbkd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173253-pwxzkbkd/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173253-pwxzkbkd/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.35543255507946\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.7539049094473874\n",
            "1\n",
            "The running loss is:\n",
            "19.27651271224022\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7139449152681563\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173253-pwxzkbkd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173253-pwxzkbkd/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.351722501218319\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.4574712037488266\n",
            "1\n",
            "The running loss is:\n",
            "9.813126027584076\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.36344911213274356\n",
            "The running loss is:\n",
            "6.200845863670111\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.2296609579137078\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173253-pwxzkbkd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173253-pwxzkbkd/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.99026171118021\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.2218615448585263\n",
            "The running loss is:\n",
            "5.51112406142056\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.20411570597853926\n",
            "1\n",
            "The running loss is:\n",
            "5.962472440674901\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.22083231261758893\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173253-pwxzkbkd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173253-pwxzkbkd/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.207641955465078\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.1928756279801881\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_32PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.wandb_agent:Running runs: ['pwxzkbkd']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_32PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173253-pwxzkbkd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173253-pwxzkbkd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173253-pwxzkbkd/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  731.740784\n",
            "66  2020-04-22  sub_region  ...             66  738.606201\n",
            "67  2020-04-23  sub_region  ...             67  737.777405\n",
            "68  2020-04-24  sub_region  ...             68  726.210022\n",
            "69  2020-04-25  sub_region  ...             69  719.908691\n",
            "70  2020-04-26  sub_region  ...             70  719.150635\n",
            "71  2020-04-27  sub_region  ...             71  720.189941\n",
            "72  2020-04-28  sub_region  ...             72  731.834473\n",
            "73  2020-04-29  sub_region  ...             73  735.288940\n",
            "74  2020-04-30  sub_region  ...             74  731.156189\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173253-pwxzkbkd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173253-pwxzkbkd/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173253-pwxzkbkd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173253-pwxzkbkd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173253-pwxzkbkd/media/plotly/test_plot_20_3857be08.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173253-pwxzkbkd/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173253-pwxzkbkd/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: pwxzkbkd \n",
            "\n"
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173253-pwxzkbkd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173253-pwxzkbkd/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173253-pwxzkbkd/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173253-pwxzkbkd/media/plotly\n",
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            "INFO:wandb.wandb_agent:Cleaning up finished run: pwxzkbkd\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
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          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 0laz5778 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 0laz5778\n"
          ],
          "name": "stdout"
        },
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          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
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        {
          "output_type": "display_data",
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/0laz5778\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/0laz5778</a><br/>\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/0laz5778\n",
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            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
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        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "22.289740599691868\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.8255459481367359\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173304-0laz5778/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173304-0laz5778/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173304-0laz5778/media/graph/graph_0_summary_42d3d77c.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173304-0laz5778/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173304-0laz5778/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173304-0laz5778/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173304-0laz5778/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173304-0laz5778/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173304-0laz5778/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.721937209367752\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.7674791559025094\n",
            "1\n",
            "The running loss is:\n",
            "19.501514554023743\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7222783168156942\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173304-0laz5778/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173304-0laz5778/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.022847175598145\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.4823276731703017\n",
            "1\n",
            "The running loss is:\n",
            "9.215275160968304\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.3413064874432705\n",
            "2\n",
            "The running loss is:\n",
            "6.453644126653671\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.23902385654272856\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173304-0laz5778/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173304-0laz5778/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.3195782247930765\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.23405845277011395\n",
            "1\n",
            "The running loss is:\n",
            "5.538634759373963\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.20513462071755417\n",
            "The running loss is:\n",
            "6.0211376789957285\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.22300509922206402\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173304-0laz5778/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173304-0laz5778/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.4065384501591325\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.2002421648207086\n",
            "Data saved to: \n",
            "28_May_202005_33PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_33PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['0laz5778']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173304-0laz5778/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "CSV Path below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173304-0laz5778/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173304-0laz5778/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  723.085754\n",
            "66  2020-04-22  sub_region  ...             66  731.862305\n",
            "67  2020-04-23  sub_region  ...             67  732.741089\n",
            "68  2020-04-24  sub_region  ...             68  723.526733\n",
            "69  2020-04-25  sub_region  ...             69  715.763306\n",
            "70  2020-04-26  sub_region  ...             70  713.154541\n",
            "71  2020-04-27  sub_region  ...             71  712.077576\n",
            "72  2020-04-28  sub_region  ...             72  725.158813\n",
            "73  2020-04-29  sub_region  ...             73  729.568176\n",
            "74  2020-04-30  sub_region  ...             74  726.045166\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173304-0laz5778/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173304-0laz5778/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173304-0laz5778/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173304-0laz5778/media/plotly/test_plot_20_967a32da.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173304-0laz5778/media\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173304-0laz5778/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 0laz5778 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173304-0laz5778/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173304-0laz5778/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173304-0laz5778/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173304-0laz5778/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173304-0laz5778/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173304-0laz5778/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 0laz5778\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: ik8pyfox with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: ik8pyfox\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/ik8pyfox\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/ik8pyfox</a><br/>\n",
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          "output_type": "stream",
          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/ik8pyfox\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmlrOHB5Zm94OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "17.69617347419262\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.6806220566997161\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173315-ik8pyfox/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173315-ik8pyfox/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173315-ik8pyfox/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173315-ik8pyfox/media/graph/graph_0_summary_71492d1d.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173315-ik8pyfox/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173315-ik8pyfox/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173315-ik8pyfox/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173315-ik8pyfox/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173315-ik8pyfox/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.942448504269123\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.4977864809334278\n",
            "1\n",
            "The running loss is:\n",
            "11.007499657571316\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.4233653714450506\n",
            "2\n",
            "The running loss is:\n",
            "7.731042757630348\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.297347798370398\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173315-ik8pyfox/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173315-ik8pyfox/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.677496463060379\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.1799037101177069\n",
            "1\n",
            "The running loss is:\n",
            "3.537908681668341\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.13607341083339775\n",
            "The running loss is:\n",
            "3.6070269970223308\n",
            "The number of items in train is: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173315-ik8pyfox/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "26\n",
            "The loss for epoch 6\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173315-ik8pyfox/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0.13873180757778195\n",
            "1\n",
            "The running loss is:\n",
            "5.469477290287614\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.21036451116490823\n",
            "2\n",
            "The running loss is:\n",
            "4.373710255138576\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.16821962519763753\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173315-ik8pyfox/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173315-ik8pyfox/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.385507521219552\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.168673366200752\n",
            "Data saved to: \n",
            "28_May_202005_33PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_33PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['ik8pyfox']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173315-ik8pyfox/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173315-ik8pyfox/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173315-ik8pyfox/wandb-summary.json\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  641.062012\n",
            "66  2020-04-22  sub_region  ...             66  649.469788\n",
            "67  2020-04-23  sub_region  ...             67  662.655884\n",
            "68  2020-04-24  sub_region  ...             68  663.000610\n",
            "69  2020-04-25  sub_region  ...             69  652.813110\n",
            "70  2020-04-26  sub_region  ...             70  647.735291\n",
            "71  2020-04-27  sub_region  ...             71  634.131409\n",
            "72  2020-04-28  sub_region  ...             72  641.305664\n",
            "73  2020-04-29  sub_region  ...             73  641.875549\n",
            "74  2020-04-30  sub_region  ...             74  656.309570\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173315-ik8pyfox/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173315-ik8pyfox/wandb-history.jsonl\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173315-ik8pyfox/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173315-ik8pyfox/media/plotly/test_plot_20_7320596b.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173315-ik8pyfox/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173315-ik8pyfox/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: ik8pyfox \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173315-ik8pyfox/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173315-ik8pyfox/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173315-ik8pyfox/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173315-ik8pyfox/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173315-ik8pyfox/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173315-ik8pyfox/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: ik8pyfox\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 86vfy9ok with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 86vfy9ok\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/86vfy9ok\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/86vfy9ok</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/86vfy9ok\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjg2dmZ5OW9rOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "17.936274334788322\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.6898567051841662\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173326-86vfy9ok/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173326-86vfy9ok/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173326-86vfy9ok/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173326-86vfy9ok/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173326-86vfy9ok/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173326-86vfy9ok/media/graph/graph_0_summary_52568ab3.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173326-86vfy9ok/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173326-86vfy9ok/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173326-86vfy9ok/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.563346169888973\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.48320562191880667\n",
            "1\n",
            "The running loss is:\n",
            "10.63600341975689\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.4090770546060342\n",
            "2\n",
            "The running loss is:\n",
            "7.032686905935407\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.27048795792059255\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173326-86vfy9ok/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173326-86vfy9ok/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.1291360929608345\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.1588129266523398\n",
            "1\n",
            "The running loss is:\n",
            "3.5215289955958724\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.13544342290753356\n",
            "The running loss is:\n",
            "4.55407845415175\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.17515686362122113\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173326-86vfy9ok/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173326-86vfy9ok/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "3.83646054379642\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.14755617476140076\n",
            "2\n",
            "The running loss is:\n",
            "4.684140918310732\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.18015926608887428\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173326-86vfy9ok/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173326-86vfy9ok/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.284909620881081\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.28018883157234925\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_33PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_33PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['86vfy9ok']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173326-86vfy9ok/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173326-86vfy9ok/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173326-86vfy9ok/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  811.324219\n",
            "66  2020-04-22  sub_region  ...             66  801.534241\n",
            "67  2020-04-23  sub_region  ...             67  837.117920\n",
            "68  2020-04-24  sub_region  ...             68  854.320984\n",
            "69  2020-04-25  sub_region  ...             69  834.952698\n",
            "70  2020-04-26  sub_region  ...             70  831.260376\n",
            "71  2020-04-27  sub_region  ...             71  782.874146\n",
            "72  2020-04-28  sub_region  ...             72  832.120972\n",
            "73  2020-04-29  sub_region  ...             73  785.114258\n",
            "74  2020-04-30  sub_region  ...             74  817.378418\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173326-86vfy9ok/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173326-86vfy9ok/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173326-86vfy9ok/wandb-history.jsonl\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173326-86vfy9ok/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173326-86vfy9ok/media/plotly/test_plot_20_d2368e1b.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173326-86vfy9ok/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173326-86vfy9ok/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 86vfy9ok \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173326-86vfy9ok/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173326-86vfy9ok/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173326-86vfy9ok/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173326-86vfy9ok/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173326-86vfy9ok/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173326-86vfy9ok/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 86vfy9ok\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: xlla4e7k with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: xlla4e7k\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/xlla4e7k\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/xlla4e7k</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/xlla4e7k\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnhsbGE0ZTdrOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "16.614094384014606\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.639003630154408\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173337-xlla4e7k/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173337-xlla4e7k/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173337-xlla4e7k/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173337-xlla4e7k/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173337-xlla4e7k/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173337-xlla4e7k/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173337-xlla4e7k/media/graph/graph_0_summary_d7ce5ecf.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173337-xlla4e7k/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173337-xlla4e7k/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.784377932548523\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.49170684355955857\n",
            "1\n",
            "The running loss is:\n",
            "10.317914854735136\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.3968428790282745\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173337-xlla4e7k/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173337-xlla4e7k/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.098226331174374\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.31147024350670666\n",
            "1\n",
            "The running loss is:\n",
            "5.721522863255814\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.22005857166368514\n",
            "The running loss is:\n",
            "4.689897304400802\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.180380665553877\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173337-xlla4e7k/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173337-xlla4e7k/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.495792439207435\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.24983817073874748\n",
            "1\n",
            "The running loss is:\n",
            "5.418599771335721\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.20840768351291233\n",
            "The running loss is:\n",
            "4.195066409185529\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.16134870804559726\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173337-xlla4e7k/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173337-xlla4e7k/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.375888114795089\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.2067649274921188\n",
            "2\n",
            "Data saved to: \n",
            "28_May_202005_33PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_33PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['xlla4e7k']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173337-xlla4e7k/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173337-xlla4e7k/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173337-xlla4e7k/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  730.216797\n",
            "66  2020-04-22  sub_region  ...             66  741.966797\n",
            "67  2020-04-23  sub_region  ...             67  738.005005\n",
            "68  2020-04-24  sub_region  ...             68  724.475220\n",
            "69  2020-04-25  sub_region  ...             69  719.175964\n",
            "70  2020-04-26  sub_region  ...             70  717.805908\n",
            "71  2020-04-27  sub_region  ...             71  714.661133\n",
            "72  2020-04-28  sub_region  ...             72  731.062378\n",
            "73  2020-04-29  sub_region  ...             73  725.214966\n",
            "74  2020-04-30  sub_region  ...             74  723.802612\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173337-xlla4e7k/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173337-xlla4e7k/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173337-xlla4e7k/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173337-xlla4e7k/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173337-xlla4e7k/media/plotly/test_plot_20_54bf2aea.plotly.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173337-xlla4e7k/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173337-xlla4e7k/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: xlla4e7k \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173337-xlla4e7k/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173337-xlla4e7k/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173337-xlla4e7k/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173337-xlla4e7k/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173337-xlla4e7k/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173337-xlla4e7k/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: xlla4e7k\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: nlztwjca with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: nlztwjca\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/nlztwjca\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/nlztwjca</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/nlztwjca\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOm5senR3amNhOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "16.377284348011017\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.6298955518465775\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173353-nlztwjca/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173353-nlztwjca/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173353-nlztwjca/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173353-nlztwjca/media/graph/graph_0_summary_92ff03f7.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173353-nlztwjca/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173353-nlztwjca/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173353-nlztwjca/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173353-nlztwjca/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173353-nlztwjca/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.166790530085564\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.5448765588494447\n",
            "The running loss is:\n",
            "9.428598899394274\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.3626384192074721\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173353-nlztwjca/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173353-nlztwjca/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.6330753564834595\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.2551182829416715\n",
            "2\n",
            "The running loss is:\n",
            "5.67338194232434\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.2182069977817054\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_33PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_33PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173353-nlztwjca/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173353-nlztwjca/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173353-nlztwjca/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  339.937469\n",
            "66  2020-04-22  sub_region  ...             66  348.662567\n",
            "67  2020-04-23  sub_region  ...             67  321.414612\n",
            "68  2020-04-24  sub_region  ...             68  302.783997\n",
            "69  2020-04-25  sub_region  ...             69  324.099854\n",
            "70  2020-04-26  sub_region  ...             70  317.279297\n",
            "71  2020-04-27  sub_region  ...             71  371.108948\n",
            "72  2020-04-28  sub_region  ...             72  297.625122\n",
            "73  2020-04-29  sub_region  ...             73  384.647125\n",
            "74  2020-04-30  sub_region  ...             74  332.773010\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173353-nlztwjca/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173353-nlztwjca/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "INFO:wandb.wandb_agent:Running runs: ['nlztwjca']\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173353-nlztwjca/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173353-nlztwjca/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173353-nlztwjca/media/plotly/test_plot_10_807ad6e3.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173353-nlztwjca/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173353-nlztwjca/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: nlztwjca \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173353-nlztwjca/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173353-nlztwjca/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173353-nlztwjca/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173353-nlztwjca/media/plotly/test_plot_all_11_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173353-nlztwjca/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173353-nlztwjca/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: nlztwjca\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 2ra81s8w with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 2ra81s8w\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/2ra81s8w\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/2ra81s8w</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/2ra81s8w\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjJyYTgxczh3OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173404-2ra81s8w/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173404-2ra81s8w/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173404-2ra81s8w/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173404-2ra81s8w/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173404-2ra81s8w/media/graph/graph_0_summary_737450e3.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173404-2ra81s8w/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173404-2ra81s8w/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173404-2ra81s8w/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173404-2ra81s8w/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.114518700167537\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 0\n",
            "0.5755185250059834\n",
            "The running loss is:\n",
            "40.93923968449235\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 1\n",
            "1.462115703017584\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173404-2ra81s8w/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173404-2ra81s8w/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.72650630073622\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 2\n",
            "0.6330895107405793\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173404-2ra81s8w/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173404-2ra81s8w/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.82603452168405\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 3\n",
            "0.4937869472030018\n",
            "The running loss is:\n",
            "12.235369089990854\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 4\n",
            "0.43697746749967337\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173404-2ra81s8w/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173404-2ra81s8w/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.42129836557433\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 5\n",
            "0.4436177987705118\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['2ra81s8w']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.593332045944408\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 6\n",
            "0.4140475730694431\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173404-2ra81s8w/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173404-2ra81s8w/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.022986069088802\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 7\n",
            "0.42939235961031436\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173404-2ra81s8w/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173404-2ra81s8w/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.050711158488411\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 8\n",
            "0.3946682556603004\n",
            "2\n",
            "The running loss is:\n",
            "10.95676115965398\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 9\n",
            "0.3913128985590707\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_34PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173404-2ra81s8w/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173404-2ra81s8w/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_34PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  837.403687\n",
            "66  2020-04-22  sub_region  ...             66  830.898865\n",
            "67  2020-04-23  sub_region  ...             67  824.932678\n",
            "68  2020-04-24  sub_region  ...             68  836.498291\n",
            "69  2020-04-25  sub_region  ...             69  841.817627\n",
            "70  2020-04-26  sub_region  ...             70  846.289368\n",
            "71  2020-04-27  sub_region  ...             71  830.351929\n",
            "72  2020-04-28  sub_region  ...             72  852.223999\n",
            "73  2020-04-29  sub_region  ...             73  823.185059\n",
            "74  2020-04-30  sub_region  ...             74  832.774414\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173404-2ra81s8w/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173404-2ra81s8w/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 2ra81s8w \n",
            "\n"
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          "name": "stdout"
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          "output_type": "stream",
          "text": [
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173404-2ra81s8w/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173404-2ra81s8w/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173404-2ra81s8w/media/plotly/test_plot_20_754d0a08.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173404-2ra81s8w/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173404-2ra81s8w/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173404-2ra81s8w/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173404-2ra81s8w/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 2ra81s8w\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: oouro82l with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: oouro82l\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/oouro82l\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/oouro82l</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/oouro82l\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOm9vdXJvODJsOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
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          ],
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        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173420-oouro82l/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173420-oouro82l/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173420-oouro82l/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173420-oouro82l/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173420-oouro82l/media/graph/graph_0_summary_607f15ec.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173420-oouro82l/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173420-oouro82l/requirements.txt\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173420-oouro82l/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.39689282909967\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 0\n",
            "0.5498890296107025\n",
            "The running loss is:\n",
            "44.767726235091686\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 1\n",
            "1.5988473655389888\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173420-oouro82l/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173420-oouro82l/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.245736518874764\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 2\n",
            "0.794490589959813\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173420-oouro82l/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173420-oouro82l/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.605747589841485\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 3\n",
            "0.5573481282086244\n",
            "2\n",
            "The running loss is:\n",
            "18.130321642383933\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 4\n",
            "0.6475114872279976\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_34PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173420-oouro82l/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173420-oouro82l/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_34PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['oouro82l']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  410.513519\n",
            "66  2020-04-22  sub_region  ...             66  402.391907\n",
            "67  2020-04-23  sub_region  ...             67  409.347412\n",
            "68  2020-04-24  sub_region  ...             68  416.087708\n",
            "69  2020-04-25  sub_region  ...             69  416.266327\n",
            "70  2020-04-26  sub_region  ...             70  415.997223\n",
            "71  2020-04-27  sub_region  ...             71  405.508575\n",
            "72  2020-04-28  sub_region  ...             72  415.981689\n",
            "73  2020-04-29  sub_region  ...             73  393.018555\n",
            "74  2020-04-30  sub_region  ...             74  405.671448\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173420-oouro82l/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173420-oouro82l/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: oouro82l \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173420-oouro82l/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173420-oouro82l/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173420-oouro82l/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173420-oouro82l/media/plotly/test_plot_10_d4fb56b9.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173420-oouro82l/media/plotly/test_plot_all_11_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173420-oouro82l/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173420-oouro82l/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173420-oouro82l/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: oouro82l\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: t6rqbam0 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: t6rqbam0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/t6rqbam0\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/t6rqbam0</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/t6rqbam0\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnQ2cnFiYW0wOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173434-t6rqbam0/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173434-t6rqbam0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173434-t6rqbam0/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173434-t6rqbam0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173434-t6rqbam0/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173434-t6rqbam0/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173434-t6rqbam0/media/graph/graph_0_summary_55c20091.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173434-t6rqbam0/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173434-t6rqbam0/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.718430198729038\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.3969788962492236\n",
            "The running loss is:\n",
            "28.698574017733335\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.0629101488049384\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173434-t6rqbam0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173434-t6rqbam0/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.018407717347145\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.5932743599017462\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173434-t6rqbam0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173434-t6rqbam0/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.534781254827976\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.39017708351214725\n",
            "2\n",
            "The running loss is:\n",
            "5.385364955291152\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.1994579613070797\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173434-t6rqbam0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173434-t6rqbam0/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.625352939590812\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.28242047924410413\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['t6rqbam0']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.7238668041536584\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.2490321038575429\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173434-t6rqbam0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173434-t6rqbam0/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.8772421116009355\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.29174970783707166\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173434-t6rqbam0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173434-t6rqbam0/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.317102946341038\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.27100381282744584\n",
            "1\n",
            "The running loss is:\n",
            "5.420254968106747\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.20075018400395359\n",
            "Data saved to: \n",
            "28_May_202005_34PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173434-t6rqbam0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173434-t6rqbam0/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_34PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  779.043823\n",
            "66  2020-04-22  sub_region  ...             66  778.755005\n",
            "67  2020-04-23  sub_region  ...             67  778.881592\n",
            "68  2020-04-24  sub_region  ...             68  779.025024\n",
            "69  2020-04-25  sub_region  ...             69  779.112366\n",
            "70  2020-04-26  sub_region  ...             70  779.083008\n",
            "71  2020-04-27  sub_region  ...             71  778.987671\n",
            "72  2020-04-28  sub_region  ...             72  778.948608\n",
            "73  2020-04-29  sub_region  ...             73  778.723877\n",
            "74  2020-04-30  sub_region  ...             74  778.891602\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173434-t6rqbam0/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173434-t6rqbam0/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173434-t6rqbam0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173434-t6rqbam0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173434-t6rqbam0/media/plotly/test_plot_20_03adc1c1.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173434-t6rqbam0/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173434-t6rqbam0/media/plotly\n"
          ],
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          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: t6rqbam0 \n",
            "\n"
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          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173434-t6rqbam0/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173434-t6rqbam0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173434-t6rqbam0/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173434-t6rqbam0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173434-t6rqbam0/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173434-t6rqbam0/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: t6rqbam0\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: fzr1hshg with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: fzr1hshg\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/fzr1hshg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/fzr1hshg</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/fzr1hshg\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmZ6cjFoc2hnOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173451-fzr1hshg/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173451-fzr1hshg/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173451-fzr1hshg/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173451-fzr1hshg/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173451-fzr1hshg/wandb-events.jsonl\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173451-fzr1hshg/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.668751269578934\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.3951389359103309\n",
            "The running loss is:\n",
            "28.521158926188946\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.0563392194884795\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173451-fzr1hshg/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173451-fzr1hshg/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.176970317959785\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.5250729747392513\n",
            "The running loss is:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173451-fzr1hshg/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173451-fzr1hshg/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "6.591888493858278\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.24414401829104732\n",
            "1\n",
            "The running loss is:\n",
            "7.487370473332703\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.27731001753084084\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173451-fzr1hshg/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173451-fzr1hshg/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.26125293970108\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.1948612199889289\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['fzr1hshg']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.058157247491181\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.22437619435152523\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173451-fzr1hshg/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173451-fzr1hshg/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.886977478861809\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.18099916588377069\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173451-fzr1hshg/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173451-fzr1hshg/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.049873136915267\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.29814344951538024\n",
            "1\n",
            "The running loss is:\n",
            "6.706313902512193\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.24838199638934047\n",
            "2\n",
            "Data saved to: \n",
            "28_May_202005_34PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173451-fzr1hshg/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_34PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  755.005249\n",
            "66  2020-04-22  sub_region  ...             66  751.443115\n",
            "67  2020-04-23  sub_region  ...             67  753.844849\n",
            "68  2020-04-24  sub_region  ...             68  755.624390\n",
            "69  2020-04-25  sub_region  ...             69  755.841370\n",
            "70  2020-04-26  sub_region  ...             70  755.883606\n",
            "71  2020-04-27  sub_region  ...             71  752.995850\n",
            "72  2020-04-28  sub_region  ...             72  755.255066\n",
            "73  2020-04-29  sub_region  ...             73  751.328247\n",
            "74  2020-04-30  sub_region  ...             74  753.881714\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173451-fzr1hshg/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173451-fzr1hshg/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: fzr1hshg \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173451-fzr1hshg/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173451-fzr1hshg/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173451-fzr1hshg/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173451-fzr1hshg/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173451-fzr1hshg/media/plotly/test_plot_20_9c0dfd36.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173451-fzr1hshg/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173451-fzr1hshg/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173451-fzr1hshg/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: fzr1hshg\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 1rhqvm3o with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 1rhqvm3o\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/1rhqvm3o\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/1rhqvm3o</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/1rhqvm3o\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjFyaHF2bTNvOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173508-1rhqvm3o/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173508-1rhqvm3o/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173508-1rhqvm3o/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173508-1rhqvm3o/media/graph/graph_0_summary_a757ef87.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173508-1rhqvm3o/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173508-1rhqvm3o/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173508-1rhqvm3o/wandb-events.jsonl\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173508-1rhqvm3o/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "23.849574618041515\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.883317578445982\n",
            "The running loss is:\n",
            "20.568836465477943\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.7618087579806646\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173508-1rhqvm3o/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173508-1rhqvm3o/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.27038937062025\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7137181248377871\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173508-1rhqvm3o/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173508-1rhqvm3o/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.25364562869072\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.6390239121737303\n",
            "1\n",
            "The running loss is:\n",
            "14.78194186091423\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.5474793281820085\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173508-1rhqvm3o/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173508-1rhqvm3o/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.99204146116972\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.40711264670998965\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['1rhqvm3o']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "34.4778664149344\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "1.2769580153679405\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173508-1rhqvm3o/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173508-1rhqvm3o/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.340780273079872\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.6792881582622174\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173508-1rhqvm3o/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173508-1rhqvm3o/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.76011623442173\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.6948191197933974\n",
            "2\n",
            "The running loss is:\n",
            "18.743604812771082\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.6942075856581882\n",
            "Data saved to: \n",
            "28_May_202005_35PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_35PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173508-1rhqvm3o/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173508-1rhqvm3o/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173508-1rhqvm3o/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  351.287323\n",
            "66  2020-04-22  sub_region  ...             66  351.352722\n",
            "67  2020-04-23  sub_region  ...             67  351.261353\n",
            "68  2020-04-24  sub_region  ...             68  351.227020\n",
            "69  2020-04-25  sub_region  ...             69  351.256317\n",
            "70  2020-04-26  sub_region  ...             70  351.258667\n",
            "71  2020-04-27  sub_region  ...             71  351.388000\n",
            "72  2020-04-28  sub_region  ...             72  351.195374\n",
            "73  2020-04-29  sub_region  ...             73  351.413818\n",
            "74  2020-04-30  sub_region  ...             74  351.286987\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173508-1rhqvm3o/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173508-1rhqvm3o/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173508-1rhqvm3o/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173508-1rhqvm3o/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173508-1rhqvm3o/media/plotly/test_plot_20_3234c249.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173508-1rhqvm3o/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173508-1rhqvm3o/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 1rhqvm3o \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173508-1rhqvm3o/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173508-1rhqvm3o/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173508-1rhqvm3o/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173508-1rhqvm3o/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173508-1rhqvm3o/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173508-1rhqvm3o/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 1rhqvm3o\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: hb05ymdu with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: hb05ymdu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/hb05ymdu\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/hb05ymdu</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/hb05ymdu\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmhiMDV5bWR1OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173524-hb05ymdu/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173524-hb05ymdu/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173524-hb05ymdu/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173524-hb05ymdu/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173524-hb05ymdu/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173524-hb05ymdu/media/graph/graph_0_summary_69776693.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173524-hb05ymdu/wandb-summary.json\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173524-hb05ymdu/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "23.785254307091236\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.8809353447070828\n",
            "The running loss is:\n",
            "20.365187123417854\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.7542661897562168\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173524-hb05ymdu/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173524-hb05ymdu/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.407820589840412\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7188081699940894\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173524-hb05ymdu/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173524-hb05ymdu/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.047366067767143\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.594346891398783\n",
            "1\n",
            "The running loss is:\n",
            "16.049381844699383\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.5944215498036809\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173524-hb05ymdu/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173524-hb05ymdu/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.695411691442132\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.3220522848682271\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['hb05ymdu']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.989098764955997\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.4440406949983703\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173524-hb05ymdu/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173524-hb05ymdu/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.975145380944014\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.48056094003496347\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173524-hb05ymdu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173524-hb05ymdu/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "6.981494817882776\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.25857388214380655\n",
            "The running loss is:\n",
            "7.491051498800516\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.27744635180742655\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_35PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173524-hb05ymdu/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173524-hb05ymdu/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_35PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  709.696960\n",
            "66  2020-04-22  sub_region  ...             66  709.161499\n",
            "67  2020-04-23  sub_region  ...             67  708.643188\n",
            "68  2020-04-24  sub_region  ...             68  708.597656\n",
            "69  2020-04-25  sub_region  ...             69  708.908020\n",
            "70  2020-04-26  sub_region  ...             70  708.755493\n",
            "71  2020-04-27  sub_region  ...             71  709.836182\n",
            "72  2020-04-28  sub_region  ...             72  708.476990\n",
            "73  2020-04-29  sub_region  ...             73  709.191528\n",
            "74  2020-04-30  sub_region  ...             74  708.709473\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173524-hb05ymdu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173524-hb05ymdu/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173524-hb05ymdu/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173524-hb05ymdu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173524-hb05ymdu/media/plotly/test_plot_20_509b02c0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173524-hb05ymdu/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173524-hb05ymdu/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: hb05ymdu \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173524-hb05ymdu/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173524-hb05ymdu/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173524-hb05ymdu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173524-hb05ymdu/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173524-hb05ymdu/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173524-hb05ymdu/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: hb05ymdu\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: a81xvfoe with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: a81xvfoe\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/a81xvfoe\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/a81xvfoe</a><br/>\n",
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          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
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          "output_type": "stream",
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173540-a81xvfoe/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173540-a81xvfoe/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173540-a81xvfoe/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173540-a81xvfoe/media/graph/graph_0_summary_c9c3fc1e.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173540-a81xvfoe/wandb-history.jsonl\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173540-a81xvfoe/wandb-summary.json\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "25.31017677485943\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.9734683374945934\n",
            "The running loss is:\n",
            "17.691114902496338\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.6804274962498591\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173540-a81xvfoe/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173540-a81xvfoe/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.284134477376938\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.8186205568221899\n",
            "The running loss is:\n",
            "14.811767756938934\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.5696833752668821\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173540-a81xvfoe/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173540-a81xvfoe/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.015214286744595\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.46212362641325366\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173540-a81xvfoe/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173540-a81xvfoe/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.353260762989521\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.3982023370380585\n",
            "2\n",
            "The running loss is:\n",
            "7.667264841496944\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.2948948015960363\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_35PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.wandb_agent:Running runs: ['a81xvfoe']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173540-a81xvfoe/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173540-a81xvfoe/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_35PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  318.850616\n",
            "66  2020-04-22  sub_region  ...             66  316.706177\n",
            "67  2020-04-23  sub_region  ...             67  320.013977\n",
            "68  2020-04-24  sub_region  ...             68  321.565765\n",
            "69  2020-04-25  sub_region  ...             69  321.352051\n",
            "70  2020-04-26  sub_region  ...             70  320.382446\n",
            "71  2020-04-27  sub_region  ...             71  318.602081\n",
            "72  2020-04-28  sub_region  ...             72  318.317017\n",
            "73  2020-04-29  sub_region  ...             73  318.346680\n",
            "74  2020-04-30  sub_region  ...             74  320.484741\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173540-a81xvfoe/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173540-a81xvfoe/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: a81xvfoe \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173540-a81xvfoe/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173540-a81xvfoe/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173540-a81xvfoe/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173540-a81xvfoe/media/plotly/test_plot_all_15_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173540-a81xvfoe/media/plotly/test_plot_14_b1c54b64.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173540-a81xvfoe/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173540-a81xvfoe/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173540-a81xvfoe/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: a81xvfoe\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: gg0yv0kt with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: gg0yv0kt\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/gg0yv0kt\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/gg0yv0kt</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/gg0yv0kt\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmdnMHl2MGt0OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173551-gg0yv0kt/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173551-gg0yv0kt/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173551-gg0yv0kt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173551-gg0yv0kt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173551-gg0yv0kt/media/graph/graph_0_summary_6680c38e.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173551-gg0yv0kt/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173551-gg0yv0kt/wandb-events.jsonl\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173551-gg0yv0kt/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "25.090075597167015\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.9650029075833467\n",
            "The running loss is:\n",
            "17.45224541425705\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.6712402082406558\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173551-gg0yv0kt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173551-gg0yv0kt/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.34785506129265\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.8210713485112557\n",
            "The running loss is:\n",
            "14.617094554007053\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.5621959443848866\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173551-gg0yv0kt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173551-gg0yv0kt/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "12.857044279575348\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.4945017030605903\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173551-gg0yv0kt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173551-gg0yv0kt/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.908861950039864\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.4195716134630717\n",
            "1\n",
            "The running loss is:\n",
            "7.671748608350754\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.2950672541673367\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['gg0yv0kt']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173551-gg0yv0kt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173551-gg0yv0kt/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.437005430459976\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.2475771319407683\n",
            "The running loss is:\n",
            "7.865247845649719\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.3025095325249892\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173551-gg0yv0kt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173551-gg0yv0kt/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.2042678743600845\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.2001641490138494\n",
            "Data saved to: \n",
            "28_May_202005_35PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_35PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173551-gg0yv0kt/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173551-gg0yv0kt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173551-gg0yv0kt/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  570.512756\n",
            "66  2020-04-22  sub_region  ...             66  571.143372\n",
            "67  2020-04-23  sub_region  ...             67  570.903015\n",
            "68  2020-04-24  sub_region  ...             68  570.691162\n",
            "69  2020-04-25  sub_region  ...             69  570.595093\n",
            "70  2020-04-26  sub_region  ...             70  570.591003\n",
            "71  2020-04-27  sub_region  ...             71  570.901611\n",
            "72  2020-04-28  sub_region  ...             72  570.663696\n",
            "73  2020-04-29  sub_region  ...             73  571.259827\n",
            "74  2020-04-30  sub_region  ...             74  571.026001\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173551-gg0yv0kt/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173551-gg0yv0kt/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: gg0yv0kt \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173551-gg0yv0kt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173551-gg0yv0kt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173551-gg0yv0kt/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173551-gg0yv0kt/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173551-gg0yv0kt/media/plotly/test_plot_20_96e49c04.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173551-gg0yv0kt/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173551-gg0yv0kt/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173551-gg0yv0kt/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: gg0yv0kt\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 695h83kt with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 695h83kt\n"
          ],
          "name": "stdout"
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          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/695h83kt\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/695h83kt</a><br/>\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/695h83kt\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjY5NWg4M2t0OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
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          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173607-695h83kt/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173607-695h83kt/media/graph/graph_0_summary_f71ffa02.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173607-695h83kt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173607-695h83kt/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173607-695h83kt/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173607-695h83kt/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173607-695h83kt/wandb-history.jsonl\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "24.998789221048355\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.9614918931172445\n",
            "The running loss is:\n",
            "19.62556444108486\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.754829401580187\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173607-695h83kt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173607-695h83kt/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.021592140197754\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.7315996976999136\n",
            "The running loss is:\n",
            "13.53062754869461\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.5204087518728696\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173607-695h83kt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173607-695h83kt/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.939093425869942\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.45919590099499774\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173607-695h83kt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173607-695h83kt/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.370361641049385\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.475783140040361\n",
            "1\n",
            "The running loss is:\n",
            "10.105389013886452\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.388668808226402\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['695h83kt']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173607-695h83kt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173607-695h83kt/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.374523870646954\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.20671245656334436\n",
            "1\n",
            "The running loss is:\n",
            "4.875691753812134\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.18752660591585132\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173607-695h83kt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173607-695h83kt/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.123469600221142\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.27397960000850546\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_36PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_36PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173607-695h83kt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173607-695h83kt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173607-695h83kt/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  697.734924\n",
            "66  2020-04-22  sub_region  ...             66  699.246887\n",
            "67  2020-04-23  sub_region  ...             67  698.447754\n",
            "68  2020-04-24  sub_region  ...             68  697.592102\n",
            "69  2020-04-25  sub_region  ...             69  697.349731\n",
            "70  2020-04-26  sub_region  ...             70  697.215332\n",
            "71  2020-04-27  sub_region  ...             71  698.537048\n",
            "72  2020-04-28  sub_region  ...             72  697.177002\n",
            "73  2020-04-29  sub_region  ...             73  699.459961\n",
            "74  2020-04-30  sub_region  ...             74  698.878723\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173607-695h83kt/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173607-695h83kt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173607-695h83kt/wandb-summary.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173607-695h83kt/media/plotly/test_plot_20_70b23860.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173607-695h83kt/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173607-695h83kt/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 695h83kt \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173607-695h83kt/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173607-695h83kt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173607-695h83kt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173607-695h83kt/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173607-695h83kt/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173607-695h83kt/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 695h83kt\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: cd3yn44i with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: cd3yn44i\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/cd3yn44i\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/cd3yn44i</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/cd3yn44i\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmNkM3luNDRpOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173623-cd3yn44i/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173623-cd3yn44i/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173623-cd3yn44i/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173623-cd3yn44i/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173623-cd3yn44i/media/graph/graph_0_summary_8d8da9f6.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173623-cd3yn44i/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173623-cd3yn44i/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173623-cd3yn44i/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173623-cd3yn44i/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "24.827137678861618\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.9548899107254468\n",
            "The running loss is:\n",
            "19.719864428043365\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.758456324155514\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173623-cd3yn44i/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173623-cd3yn44i/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.63474379479885\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.7167209151845712\n",
            "The running loss is:\n",
            "13.806637480854988\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.5310245184944227\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173623-cd3yn44i/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173623-cd3yn44i/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "11.331828974187374\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.4358395759302836\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173623-cd3yn44i/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173623-cd3yn44i/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.775343157351017\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.4144362752827314\n",
            "1\n",
            "The running loss is:\n",
            "10.32153557986021\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.3969821376869312\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['cd3yn44i']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173623-cd3yn44i/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173623-cd3yn44i/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.037654765415937\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.2706790294390745\n",
            "The running loss is:\n",
            "14.718151992186904\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.5660827689302655\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173623-cd3yn44i/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173623-cd3yn44i/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.737017912324518\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.22065453508940452\n",
            "Data saved to: \n",
            "28_May_202005_36PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_36PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173623-cd3yn44i/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173623-cd3yn44i/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173623-cd3yn44i/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  632.474121\n",
            "66  2020-04-22  sub_region  ...             66  634.329102\n",
            "67  2020-04-23  sub_region  ...             67  632.773071\n",
            "68  2020-04-24  sub_region  ...             68  631.713074\n",
            "69  2020-04-25  sub_region  ...             69  631.196228\n",
            "70  2020-04-26  sub_region  ...             70  631.151917\n",
            "71  2020-04-27  sub_region  ...             71  632.160889\n",
            "72  2020-04-28  sub_region  ...             72  631.541016\n",
            "73  2020-04-29  sub_region  ...             73  633.977478\n",
            "74  2020-04-30  sub_region  ...             74  632.605713\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173623-cd3yn44i/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173623-cd3yn44i/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173623-cd3yn44i/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173623-cd3yn44i/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173623-cd3yn44i/media/plotly/test_plot_20_a4b0535b.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173623-cd3yn44i/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173623-cd3yn44i/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: cd3yn44i \n",
            "\n"
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          "output_type": "stream",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173623-cd3yn44i/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173623-cd3yn44i/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173623-cd3yn44i/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173623-cd3yn44i/media/plotly\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173623-cd3yn44i/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: cd3yn44i\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
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          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: ew8yx2l9 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: ew8yx2l9\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/ew8yx2l9\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/ew8yx2l9</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/ew8yx2l9\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmV3OHl4Mmw5OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
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        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173642-ew8yx2l9/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173642-ew8yx2l9/media/graph/graph_0_summary_17923498.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173642-ew8yx2l9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173642-ew8yx2l9/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173642-ew8yx2l9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173642-ew8yx2l9/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173642-ew8yx2l9/wandb-metadata.json\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173642-ew8yx2l9/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.056726850569248\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 0\n",
            "0.5734545303774732\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173642-ew8yx2l9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173642-ew8yx2l9/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "46.208357490599155\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 1\n",
            "1.6502984818071127\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173642-ew8yx2l9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173642-ew8yx2l9/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "29.032308392226696\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 2\n",
            "1.0368681568652391\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173642-ew8yx2l9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173642-ew8yx2l9/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "28.841471418738365\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 3\n",
            "1.0300525506692273\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['ew8yx2l9']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173642-ew8yx2l9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173642-ew8yx2l9/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "28.338371301069856\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 4\n",
            "1.0120846893239235\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173642-ew8yx2l9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173642-ew8yx2l9/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "28.69572694809176\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 5\n",
            "1.0248473910032772\n",
            "2\n",
            "The running loss is:\n",
            "19.646056853234768\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 6\n",
            "0.7016448876155275\n",
            "3\n",
            "Stopping model now\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173642-ew8yx2l9/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173642-ew8yx2l9/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "28_May_202005_36PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_36PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  429.154022\n",
            "66  2020-04-22  sub_region  ...             66  428.729492\n",
            "67  2020-04-23  sub_region  ...             67  429.150635\n",
            "68  2020-04-24  sub_region  ...             68  429.542450\n",
            "69  2020-04-25  sub_region  ...             69  429.516174\n",
            "70  2020-04-26  sub_region  ...             70  429.553772\n",
            "71  2020-04-27  sub_region  ...             71  428.790649\n",
            "72  2020-04-28  sub_region  ...             72  429.774963\n",
            "73  2020-04-29  sub_region  ...             73  428.176636\n",
            "74  2020-04-30  sub_region  ...             74  428.942596\n",
            "\n",
            "[20 rows x 28 columns]"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173642-ew8yx2l9/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173642-ew8yx2l9/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173642-ew8yx2l9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173642-ew8yx2l9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173642-ew8yx2l9/media/plotly/test_plot_14_bdfadf79.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173642-ew8yx2l9/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173642-ew8yx2l9/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: ew8yx2l9 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173642-ew8yx2l9/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173642-ew8yx2l9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173642-ew8yx2l9/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173642-ew8yx2l9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173642-ew8yx2l9/media/plotly/test_plot_all_15_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173642-ew8yx2l9/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: ew8yx2l9\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: kqq3a8gv with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: kqq3a8gv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/kqq3a8gv\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/kqq3a8gv</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/kqq3a8gv\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmtxcTNhOGd2OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173659-kqq3a8gv/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173659-kqq3a8gv/media/graph/graph_0_summary_55480c76.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173659-kqq3a8gv/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173659-kqq3a8gv/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173659-kqq3a8gv/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173659-kqq3a8gv/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173659-kqq3a8gv/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173659-kqq3a8gv/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173659-kqq3a8gv/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.879746047779918\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 0\n",
            "0.4957052159921399\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173659-kqq3a8gv/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173659-kqq3a8gv/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "50.16468261182308\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 1\n",
            "1.7915958075651102\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173659-kqq3a8gv/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173659-kqq3a8gv/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "28.752860818058252\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 2\n",
            "1.0268878863592232\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173659-kqq3a8gv/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173659-kqq3a8gv/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "28.75844579562545\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 3\n",
            "1.027087349843766\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['kqq3a8gv']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173659-kqq3a8gv/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173659-kqq3a8gv/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "28.228818399831653\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 4\n",
            "1.0081720857082732\n",
            "1\n",
            "The running loss is:\n",
            "28.275303000351414\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173659-kqq3a8gv/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The number of items in train is: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173659-kqq3a8gv/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "28\n",
            "The loss for epoch 5\n",
            "1.0098322500125505\n",
            "2\n",
            "The running loss is:\n",
            "19.328091056086123\n",
            "The number of items in train is: \n",
            "28\n",
            "The loss for epoch 6\n",
            "0.6902889662887901\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_37PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173659-kqq3a8gv/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173659-kqq3a8gv/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_37PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  429.017181\n",
            "66  2020-04-22  sub_region  ...             66  428.986389\n",
            "67  2020-04-23  sub_region  ...             67  429.089111\n",
            "68  2020-04-24  sub_region  ...             68  429.224731\n",
            "69  2020-04-25  sub_region  ...             69  429.164246\n",
            "70  2020-04-26  sub_region  ...             70  429.191895\n",
            "71  2020-04-27  sub_region  ...             71  428.905151\n",
            "72  2020-04-28  sub_region  ...             72  429.181915\n",
            "73  2020-04-29  sub_region  ...             73  428.645477\n",
            "74  2020-04-30  sub_region  ...             74  428.955353\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173659-kqq3a8gv/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173659-kqq3a8gv/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: kqq3a8gv \n",
            "\n"
          ],
          "name": "stdout"
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          "output_type": "stream",
          "text": [
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173659-kqq3a8gv/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173659-kqq3a8gv/media/plotly/test_plot_all_15_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173659-kqq3a8gv/media/plotly/test_plot_14_562144b7.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173659-kqq3a8gv/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173659-kqq3a8gv/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173659-kqq3a8gv/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173659-kqq3a8gv/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: kqq3a8gv\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: x7pfw8a7 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: x7pfw8a7\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/x7pfw8a7\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/x7pfw8a7</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/x7pfw8a7\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOng3cGZ3OGE3OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
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        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173715-x7pfw8a7/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173715-x7pfw8a7/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173715-x7pfw8a7/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173715-x7pfw8a7/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173715-x7pfw8a7/media/graph/graph_0_summary_d2057918.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173715-x7pfw8a7/wandb-summary.json\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.515796359628439\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.3524369022084607\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173715-x7pfw8a7/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173715-x7pfw8a7/wandb-history.jsonl\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "31.958917625248432\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.1836636157499418\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173715-x7pfw8a7/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173715-x7pfw8a7/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.400996170938015\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.6444813396643709\n",
            "1\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173715-x7pfw8a7/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173715-x7pfw8a7/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.490230657160282\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.647786320635566\n"
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          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['x7pfw8a7']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173715-x7pfw8a7/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173715-x7pfw8a7/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.88597560673952\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.6624435409903526\n",
            "The running loss is:\n",
            "17.275350011885166\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.6398277782179691\n"
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          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173715-x7pfw8a7/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173715-x7pfw8a7/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.80729231238365\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.6595293449030982\n"
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          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173715-x7pfw8a7/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173715-x7pfw8a7/wandb-history.jsonl\n"
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        {
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          "text": [
            "The running loss is:\n",
            "17.339848961681128\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.6422166282104121\n"
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        {
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          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173715-x7pfw8a7/wandb-summary.json\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.749763071537018\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.6203615952421118\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173715-x7pfw8a7/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173715-x7pfw8a7/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.160026656463742\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.6355565428319905\n",
            "Data saved to: \n",
            "28_May_202005_37PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_37PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173715-x7pfw8a7/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173715-x7pfw8a7/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173715-x7pfw8a7/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  452.964355\n",
            "66  2020-04-22  sub_region  ...             66  448.373505\n",
            "67  2020-04-23  sub_region  ...             67  451.731903\n",
            "68  2020-04-24  sub_region  ...             68  454.205078\n",
            "69  2020-04-25  sub_region  ...             69  454.852173\n",
            "70  2020-04-26  sub_region  ...             70  454.945068\n",
            "71  2020-04-27  sub_region  ...             71  451.557831\n",
            "72  2020-04-28  sub_region  ...             72  453.470245\n",
            "73  2020-04-29  sub_region  ...             73  448.514465\n",
            "74  2020-04-30  sub_region  ...             74  451.503876\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173715-x7pfw8a7/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173715-x7pfw8a7/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: x7pfw8a7 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173715-x7pfw8a7/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173715-x7pfw8a7/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173715-x7pfw8a7/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173715-x7pfw8a7/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173715-x7pfw8a7/media/plotly/test_plot_20_da2b038c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173715-x7pfw8a7/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173715-x7pfw8a7/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173715-x7pfw8a7/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: x7pfw8a7\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 83bi2wlq with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 83bi2wlq\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/83bi2wlq\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/83bi2wlq</a><br/>\n",
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            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/83bi2wlq\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjgzYmkyd2xxOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173736-83bi2wlq/config.yaml\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173736-83bi2wlq/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173736-83bi2wlq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173736-83bi2wlq/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173736-83bi2wlq/requirements.txt\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.540501032024622\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.353351890074986\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173736-83bi2wlq/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173736-83bi2wlq/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "32.01947431452572\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.1859064560935453\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173736-83bi2wlq/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173736-83bi2wlq/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.433912321925163\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.6457004563675987\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173736-83bi2wlq/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173736-83bi2wlq/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.372653737664223\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.6434316199134897\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['83bi2wlq']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173736-83bi2wlq/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173736-83bi2wlq/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.937954049557447\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.6643686685021277\n",
            "The running loss is:\n",
            "17.54516338557005\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.649820866132224\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173736-83bi2wlq/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173736-83bi2wlq/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.718922704458237\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.6562563964614162\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173736-83bi2wlq/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173736-83bi2wlq/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.30660080164671\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.6409852148758041\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173736-83bi2wlq/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173736-83bi2wlq/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.718926943838596\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.6192195164384665\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173736-83bi2wlq/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173736-83bi2wlq/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.827449632808566\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.6232388752892062\n",
            "Data saved to: \n",
            "28_May_202005_37PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_37PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173736-83bi2wlq/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173736-83bi2wlq/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173736-83bi2wlq/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  457.992615\n",
            "66  2020-04-22  sub_region  ...             66  454.040710\n",
            "67  2020-04-23  sub_region  ...             67  456.812286\n",
            "68  2020-04-24  sub_region  ...             68  459.140167\n",
            "69  2020-04-25  sub_region  ...             69  459.434723\n",
            "70  2020-04-26  sub_region  ...             70  459.551544\n",
            "71  2020-04-27  sub_region  ...             71  456.045074\n",
            "72  2020-04-28  sub_region  ...             72  458.655548\n",
            "73  2020-04-29  sub_region  ...             73  453.452576\n",
            "74  2020-04-30  sub_region  ...             74  456.783661\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173736-83bi2wlq/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173736-83bi2wlq/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 83bi2wlq \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173736-83bi2wlq/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173736-83bi2wlq/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173736-83bi2wlq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173736-83bi2wlq/media/plotly/test_plot_20_c566c54f.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173736-83bi2wlq/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173736-83bi2wlq/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173736-83bi2wlq/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173736-83bi2wlq/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 83bi2wlq\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: hkxc7mnc with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: hkxc7mnc\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/hkxc7mnc\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/hkxc7mnc</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/hkxc7mnc\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmhreGM3bW5jOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/media/graph/graph_0_summary_037e9957.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "31.62208504229784\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "1.17118833489992\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.408665716648102\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.7188394709869668\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.407186299562454\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7558217147986094\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.506580412387848\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.759502978236587\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['hkxc7mnc']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.30820331722498\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.7521556784157399\n",
            "The running loss is:\n",
            "19.309663198888302\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.7151727110699371\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.341759957373142\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.679324442865672\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.737579837441444\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.6569474013867201\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.28192499279976\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.7141453701036947\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.758581429719925\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.694762275174812\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_38PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_38PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  337.341278\n",
            "66  2020-04-22  sub_region  ...             66  337.345581\n",
            "67  2020-04-23  sub_region  ...             67  337.355377\n",
            "68  2020-04-24  sub_region  ...             68  337.360901\n",
            "69  2020-04-25  sub_region  ...             69  337.360565\n",
            "70  2020-04-26  sub_region  ...             70  337.355988\n",
            "71  2020-04-27  sub_region  ...             71  337.354065\n",
            "72  2020-04-28  sub_region  ...             72  337.343140\n",
            "73  2020-04-29  sub_region  ...             73  337.349548\n",
            "74  2020-04-30  sub_region  ...             74  337.355652\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/wandb-history.jsonl\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/media/plotly/test_plot_20_e91edfe1.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: hkxc7mnc \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173758-hkxc7mnc/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: hkxc7mnc\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 684sh0pv with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 684sh0pv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/684sh0pv\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/684sh0pv</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/684sh0pv\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjY4NHNoMHB2OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/media/graph/graph_0_summary_9c008942.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "31.686953715980053\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "1.1735908783696316\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.326208144426346\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.7157854868306054\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.44809077680111\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7573366954370782\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.70396327972412\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.7668134548045971\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['684sh0pv']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.114036343991756\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.7449643090367317\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.89861784130335\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.7369858459741981\n",
            "The running loss is:\n",
            "18.967432893812656\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.7024975145856539\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "19.17745415121317\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.7102760796745619\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.25604397803545\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.713186814001313\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.80481818318367\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.6964747475253211\n",
            "Data saved to: \n",
            "28_May_202005_38PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_38PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  353.958618\n",
            "66  2020-04-22  sub_region  ...             66  353.994934\n",
            "67  2020-04-23  sub_region  ...             67  353.966064\n",
            "68  2020-04-24  sub_region  ...             68  353.948608\n",
            "69  2020-04-25  sub_region  ...             69  353.950348\n",
            "70  2020-04-26  sub_region  ...             70  353.951172\n",
            "71  2020-04-27  sub_region  ...             71  353.985535\n",
            "72  2020-04-28  sub_region  ...             72  353.945618\n",
            "73  2020-04-29  sub_region  ...             73  354.007202\n",
            "74  2020-04-30  sub_region  ...             74  353.972107\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/wandb-history.jsonl\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/media/plotly/test_plot_20_384740d4.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 684sh0pv \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173819-684sh0pv/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 684sh0pv\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: uys7i4es with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: uys7i4es\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/uys7i4es\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/uys7i4es</a><br/>\n",
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        {
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/uys7i4es\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnV5czdpNGVzOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/media/graph/graph_0_summary_571ea964.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "28.677166178822517\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "1.1029679299547122\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.185990750789642\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.737922721184217\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.213715583086014\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.777450599349462\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.579263865947723\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.5992024563826047\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['uys7i4es']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.614615187048912\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.6005621225788043\n",
            "The running loss is:\n",
            "14.192182525992393\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.5458531740766305\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "17.893233135342598\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.6882012744362538\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.887526050209999\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.6110586942388461\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.056910991668701\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.50218888429495\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.211280450224876\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.39274155577787984\n",
            "2\n",
            "Data saved to: \n",
            "28_May_202005_38PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_38PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  409.355835\n",
            "66  2020-04-22  sub_region  ...             66  409.509674\n",
            "67  2020-04-23  sub_region  ...             67  409.471558\n",
            "68  2020-04-24  sub_region  ...             68  409.399261\n",
            "69  2020-04-25  sub_region  ...             69  409.354004\n",
            "70  2020-04-26  sub_region  ...             70  409.365173\n",
            "71  2020-04-27  sub_region  ...             71  409.424011\n",
            "72  2020-04-28  sub_region  ...             72  409.376404\n",
            "73  2020-04-29  sub_region  ...             73  409.552612\n",
            "74  2020-04-30  sub_region  ...             74  409.473022\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: uys7i4es \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/media/plotly/test_plot_20_eefb9a0f.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173842-uys7i4es/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: uys7i4es\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: p90u6d7b with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: p90u6d7b\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/p90u6d7b\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/p90u6d7b</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/p90u6d7b\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnA5MHU2ZDdiOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/media/graph/graph_0_summary_6a1615dc.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "28.61015349626541\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "1.1003905190871313\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.129196166992188\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.7357383141150842\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.223093792796135\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.7778112997229283\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.592137679457664\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.599697603056064\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['p90u6d7b']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.741975575685501\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.5669990606032885\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.141681730747223\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.5439108357979701\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.65192748606205\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.6789202879254634\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.934842199087143\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.5359554691956594\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.86246071755886\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.4177869506753408\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.102634258568287\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.3501013176372418\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_39PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_39PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/wandb-summary.json\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  475.105896\n",
            "66  2020-04-22  sub_region  ...             66  475.061249\n",
            "67  2020-04-23  sub_region  ...             67  475.085632\n",
            "68  2020-04-24  sub_region  ...             68  475.112213\n",
            "69  2020-04-25  sub_region  ...             69  475.114807\n",
            "70  2020-04-26  sub_region  ...             70  475.139618\n",
            "71  2020-04-27  sub_region  ...             71  475.079346\n",
            "72  2020-04-28  sub_region  ...             72  475.173126\n",
            "73  2020-04-29  sub_region  ...             73  475.043427\n",
            "74  2020-04-30  sub_region  ...             74  475.085358\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/media/plotly/test_plot_20_114ca43c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: p90u6d7b \n",
            "\n"
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          "name": "stdout"
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          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/media/plotly\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173903-p90u6d7b/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: p90u6d7b\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 6ujfa0hd with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 6ujfa0hd\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/6ujfa0hd\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/6ujfa0hd</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/6ujfa0hd\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjZ1amZhMGhkOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
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        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 10\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173928-6ujfa0hd/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173928-6ujfa0hd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173928-6ujfa0hd/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173928-6ujfa0hd/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173928-6ujfa0hd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173928-6ujfa0hd/requirements.txt\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173928-6ujfa0hd/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "26.287698671221733\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "1.0110653335085282\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173928-6ujfa0hd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173928-6ujfa0hd/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.019511476159096\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.8469042875445806\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173928-6ujfa0hd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173928-6ujfa0hd/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.7527959048748\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.7597229194182616\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173928-6ujfa0hd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173928-6ujfa0hd/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.987582370638847\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.6149070142553403\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['6ujfa0hd']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.31729331612587\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.5891266660048411\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173928-6ujfa0hd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173928-6ujfa0hd/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.650041669607162\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.5250016026771985\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173928-6ujfa0hd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173928-6ujfa0hd/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.348268389701843\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.5133949380654556\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173928-6ujfa0hd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173928-6ujfa0hd/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.254892647266388\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.4328804864333226\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_39PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_39PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173928-6ujfa0hd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173928-6ujfa0hd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173928-6ujfa0hd/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  347.929901\n",
            "66  2020-04-22  sub_region  ...             66  348.376862\n",
            "67  2020-04-23  sub_region  ...             67  348.171692\n",
            "68  2020-04-24  sub_region  ...             68  347.880554\n",
            "69  2020-04-25  sub_region  ...             69  347.761993\n",
            "70  2020-04-26  sub_region  ...             70  347.774658\n",
            "71  2020-04-27  sub_region  ...             71  348.046265\n",
            "72  2020-04-28  sub_region  ...             72  347.813934\n",
            "73  2020-04-29  sub_region  ...             73  348.664154\n",
            "74  2020-04-30  sub_region  ...             74  348.247253\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173928-6ujfa0hd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173928-6ujfa0hd/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173928-6ujfa0hd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 6ujfa0hd \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173928-6ujfa0hd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173928-6ujfa0hd/media/plotly/test_plot_all_17_53c27da0.plotly.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173928-6ujfa0hd/media/plotly/test_plot_16_3f181331.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173928-6ujfa0hd/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173928-6ujfa0hd/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173928-6ujfa0hd/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173928-6ujfa0hd/wandb-events.jsonl\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 6ujfa0hd\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: yrbu62av with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 10\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: yrbu62av\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/yrbu62av\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/yrbu62av</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/yrbu62av\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnlyYnU2MmF2OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 10\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 10\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 10\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 10\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/media/graph/graph_0_summary_a70512b4.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/wandb-events.jsonl\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "26.332314282655716\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "1.0127813185636814\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.822422578930855\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.8393239453434944\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.969776809215546\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.7680683388159826\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.921830900013447\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.6123781115389787\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['yrbu62av']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.494909837841988\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.5959580706862303\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.208502128720284\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.5464808511046263\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.151983082294464\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.621230118549787\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.41115165501833\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.4773519867314742\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.447976488620043\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.3633837111007709\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.989659074693918\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.3842176567189969\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_39PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_39PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 10, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  578.508301\n",
            "66  2020-04-22  sub_region  ...             66  578.518188\n",
            "67  2020-04-23  sub_region  ...             67  578.516479\n",
            "68  2020-04-24  sub_region  ...             68  578.510986\n",
            "69  2020-04-25  sub_region  ...             69  578.503906\n",
            "70  2020-04-26  sub_region  ...             70  578.503906\n",
            "71  2020-04-27  sub_region  ...             71  578.500427\n",
            "72  2020-04-28  sub_region  ...             72  578.506470\n",
            "73  2020-04-29  sub_region  ...             73  578.516052\n",
            "74  2020-04-30  sub_region  ...             74  578.512817\n",
            "\n",
            "[20 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: yrbu62av \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/media/plotly/test_plot_all_21_53c27da0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/media/plotly/test_plot_20_ae1f0e4f.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_173944-yrbu62av/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: yrbu62av\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: awwp0r4a with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: awwp0r4a\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/awwp0r4a\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/awwp0r4a</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/awwp0r4a\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmF3d3AwcjRhOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "11.178941723890603\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.4140348786626149\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174000-awwp0r4a/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174000-awwp0r4a/media/graph/graph_0_summary_6d0683bd.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174000-awwp0r4a/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174000-awwp0r4a/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174000-awwp0r4a/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174000-awwp0r4a/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174000-awwp0r4a/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174000-awwp0r4a/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174000-awwp0r4a/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.482956442050636\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.5364057941500235\n",
            "1\n",
            "The running loss is:\n",
            "12.16446726070717\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.4505358244706359\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174000-awwp0r4a/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174000-awwp0r4a/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.8048704912635\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.2890692774542037\n",
            "1\n",
            "The running loss is:\n",
            "7.099390174262226\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.26294037682452687\n",
            "The running loss is:\n",
            "6.51896614592988\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.24144319058999558\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174000-awwp0r4a/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174000-awwp0r4a/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.50308578943077\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.24085502923817667\n",
            "1\n",
            "The running loss is:\n",
            "6.709017640678212\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.24848213483993378\n",
            "The running loss is:\n",
            "6.639082251727814\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.2458919352491783\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174000-awwp0r4a/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174000-awwp0r4a/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.743373372781207\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.21271753232522989\n",
            "Data saved to: \n",
            "28_May_202005_40PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_40PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['awwp0r4a']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174000-awwp0r4a/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174000-awwp0r4a/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174000-awwp0r4a/wandb-summary.json\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  758.606506\n",
            "66  2020-04-22  sub_region  ...             66  838.690002\n",
            "67  2020-04-23  sub_region  ...             67  739.158081\n",
            "68  2020-04-24  sub_region  ...             68  760.182251\n",
            "69  2020-04-25  sub_region  ...             69  791.689392\n",
            "70  2020-04-26  sub_region  ...             70  821.668945\n",
            "71  2020-04-27  sub_region  ...             71  813.191467\n",
            "72  2020-04-28  sub_region  ...             72  824.506714\n",
            "73  2020-04-29  sub_region  ...             73  810.240173\n",
            "74  2020-04-30  sub_region  ...             74  760.039307\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174000-awwp0r4a/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174000-awwp0r4a/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174000-awwp0r4a/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174000-awwp0r4a/media/plotly/test_plot_20_50b5ba08.plotly.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174000-awwp0r4a/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174000-awwp0r4a/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: awwp0r4a \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174000-awwp0r4a/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174000-awwp0r4a/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174000-awwp0r4a/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174000-awwp0r4a/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174000-awwp0r4a/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174000-awwp0r4a/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: awwp0r4a\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 2a1ypu55 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 2a1ypu55\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/2a1ypu55\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/2a1ypu55</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/2a1ypu55\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjJhMXlwdTU1OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "11.243638909421861\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.41643107071932817\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174017-2a1ypu55/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174017-2a1ypu55/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174017-2a1ypu55/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174017-2a1ypu55/media/graph/graph_0_summary_7e02471c.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174017-2a1ypu55/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174017-2a1ypu55/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174017-2a1ypu55/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174017-2a1ypu55/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174017-2a1ypu55/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.065432488219813\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.5579789810451783\n",
            "1\n",
            "The running loss is:\n",
            "9.927668149583042\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.36769141294752006\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174017-2a1ypu55/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174017-2a1ypu55/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.617937269620597\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.2821458248007629\n",
            "1\n",
            "The running loss is:\n",
            "6.723564739339054\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.2490209162718168\n",
            "The running loss is:\n",
            "7.1353028768789954\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.2642704769214443\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174017-2a1ypu55/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174017-2a1ypu55/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.726659771054983\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.21209851003907346\n",
            "2\n",
            "The running loss is:\n",
            "6.842962785856798\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.2534430661428444\n",
            "The running loss is:\n",
            "6.147264284198172\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.22767645497030267\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174017-2a1ypu55/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174017-2a1ypu55/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.772955868189456\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.25085021734035023\n",
            "Data saved to: \n",
            "28_May_202005_40PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.wandb_agent:Running runs: ['2a1ypu55']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_40PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174017-2a1ypu55/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174017-2a1ypu55/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174017-2a1ypu55/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  702.101074\n",
            "66  2020-04-22  sub_region  ...             66  799.573730\n",
            "67  2020-04-23  sub_region  ...             67  688.857788\n",
            "68  2020-04-24  sub_region  ...             68  695.529663\n",
            "69  2020-04-25  sub_region  ...             69  724.923462\n",
            "70  2020-04-26  sub_region  ...             70  761.772095\n",
            "71  2020-04-27  sub_region  ...             71  748.695679\n",
            "72  2020-04-28  sub_region  ...             72  803.100220\n",
            "73  2020-04-29  sub_region  ...             73  733.273804\n",
            "74  2020-04-30  sub_region  ...             74  718.350464\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174017-2a1ypu55/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174017-2a1ypu55/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174017-2a1ypu55/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174017-2a1ypu55/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174017-2a1ypu55/media/plotly/test_plot_20_e049cd21.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174017-2a1ypu55/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174017-2a1ypu55/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 2a1ypu55 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174017-2a1ypu55/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174017-2a1ypu55/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174017-2a1ypu55/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174017-2a1ypu55/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174017-2a1ypu55/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174017-2a1ypu55/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 2a1ypu55\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 5py2oj8v with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 5py2oj8v\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/5py2oj8v\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/5py2oj8v</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/5py2oj8v\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174033-5py2oj8v/wandb-metadata.json\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjVweTJvajh2OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "13.754779417067766\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.5094362747062136\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174033-5py2oj8v/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174033-5py2oj8v/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174033-5py2oj8v/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174033-5py2oj8v/media/graph/graph_0_summary_fbdf70f1.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174033-5py2oj8v/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174033-5py2oj8v/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174033-5py2oj8v/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174033-5py2oj8v/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.335549898445606\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.5679833295720594\n",
            "1\n",
            "The running loss is:\n",
            "12.792411483824253\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.4737930179194168\n",
            "The running loss is:\n",
            "10.463905839249492\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.38755206812035153\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174033-5py2oj8v/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174033-5py2oj8v/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "8.178016487509012\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.3028894995373708\n",
            "The running loss is:\n",
            "7.196833549998701\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.2665493907406926\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['5py2oj8v']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174033-5py2oj8v/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "7.724783644080162\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174033-5py2oj8v/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.2861030979288949\n",
            "2\n",
            "The running loss is:\n",
            "7.328360717743635\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.27142076732383835\n",
            "The running loss is:\n",
            "8.112628399394453\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.30046771849609083\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174033-5py2oj8v/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174033-5py2oj8v/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.612887730821967\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.244921767808221\n",
            "Data saved to: \n",
            "28_May_202005_40PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_40PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174033-5py2oj8v/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174033-5py2oj8v/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174033-5py2oj8v/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  785.347290\n",
            "66  2020-04-22  sub_region  ...             66  848.155396\n",
            "67  2020-04-23  sub_region  ...             67  778.021973\n",
            "68  2020-04-24  sub_region  ...             68  802.694946\n",
            "69  2020-04-25  sub_region  ...             69  814.782837\n",
            "70  2020-04-26  sub_region  ...             70  827.907227\n",
            "71  2020-04-27  sub_region  ...             71  817.994751\n",
            "72  2020-04-28  sub_region  ...             72  834.097107\n",
            "73  2020-04-29  sub_region  ...             73  812.438049\n",
            "74  2020-04-30  sub_region  ...             74  797.486328\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174033-5py2oj8v/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174033-5py2oj8v/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174033-5py2oj8v/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174033-5py2oj8v/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174033-5py2oj8v/media/plotly/test_plot_20_e9ff783e.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174033-5py2oj8v/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174033-5py2oj8v/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 5py2oj8v \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174033-5py2oj8v/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174033-5py2oj8v/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174033-5py2oj8v/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174033-5py2oj8v/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174033-5py2oj8v/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174033-5py2oj8v/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 5py2oj8v\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
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          "output_type": "stream",
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            "wandb: Agent Starting Run: f0fvvzze with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: f0fvvzze\n"
          ],
          "name": "stdout"
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          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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          "name": "stderr"
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        {
          "output_type": "display_data",
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/f0fvvzze\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/f0fvvzze</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/f0fvvzze\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmYwZnZ2enplOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
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          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "13.334896691143513\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.4938850626349449\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174050-f0fvvzze/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174050-f0fvvzze/media/graph/graph_0_summary_23078bda.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174050-f0fvvzze/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174050-f0fvvzze/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174050-f0fvvzze/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174050-f0fvvzze/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174050-f0fvvzze/requirements.txt\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.006994970142841\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.5558146285238089\n",
            "1\n",
            "The running loss is:\n",
            "11.964153863489628\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.44311680975887513\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174050-f0fvvzze/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174050-f0fvvzze/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.46366179548204\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.3505059924252607\n",
            "1\n",
            "The running loss is:\n",
            "7.935978587716818\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.29392513287840066\n",
            "The running loss is:\n",
            "7.160205790773034\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.26519280706566795\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174050-f0fvvzze/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174050-f0fvvzze/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.432532018050551\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.2752789636315019\n",
            "2\n",
            "The running loss is:\n",
            "6.930170862004161\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.256672994889043\n",
            "The running loss is:\n",
            "7.583688434679061\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.2808773494325578\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174050-f0fvvzze/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174050-f0fvvzze/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.852206656709313\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.2537854317299746\n",
            "Data saved to: \n",
            "28_May_202005_40PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_40PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['f0fvvzze']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174050-f0fvvzze/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174050-f0fvvzze/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174050-f0fvvzze/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  792.838745\n",
            "66  2020-04-22  sub_region  ...             66  839.469238\n",
            "67  2020-04-23  sub_region  ...             67  793.361755\n",
            "68  2020-04-24  sub_region  ...             68  806.429504\n",
            "69  2020-04-25  sub_region  ...             69  816.609619\n",
            "70  2020-04-26  sub_region  ...             70  828.707764\n",
            "71  2020-04-27  sub_region  ...             71  823.823608\n",
            "72  2020-04-28  sub_region  ...             72  829.279419\n",
            "73  2020-04-29  sub_region  ...             73  820.153503\n",
            "74  2020-04-30  sub_region  ...             74  806.989746\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174050-f0fvvzze/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174050-f0fvvzze/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174050-f0fvvzze/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174050-f0fvvzze/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174050-f0fvvzze/media/plotly/test_plot_20_4bc95361.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174050-f0fvvzze/media\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174050-f0fvvzze/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: f0fvvzze \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174050-f0fvvzze/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174050-f0fvvzze/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174050-f0fvvzze/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174050-f0fvvzze/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174050-f0fvvzze/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174050-f0fvvzze/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: f0fvvzze\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: yrjbk6sj with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: yrjbk6sj\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/yrjbk6sj\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/yrjbk6sj</a><br/>\n",
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          "metadata": {
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        {
          "output_type": "stream",
          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/yrjbk6sj\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnlyamJrNnNqOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "18.989742126315832\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.7303746971659936\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174106-yrjbk6sj/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174106-yrjbk6sj/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174106-yrjbk6sj/media/graph/graph_0_summary_55f1f535.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174106-yrjbk6sj/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174106-yrjbk6sj/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174106-yrjbk6sj/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174106-yrjbk6sj/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174106-yrjbk6sj/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174106-yrjbk6sj/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.38419210910797\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.6301612349656912\n",
            "1\n",
            "The running loss is:\n",
            "15.279926925897598\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.5876894971499076\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174106-yrjbk6sj/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174106-yrjbk6sj/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.945733822882175\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.4209897624185452\n",
            "1\n",
            "The running loss is:\n",
            "5.606289468705654\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.21562651802714056\n",
            "2\n",
            "The running loss is:\n",
            "4.256948810070753\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.16372880038733667\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174106-yrjbk6sj/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174106-yrjbk6sj/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.631639575585723\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.17813998367637396\n",
            "1\n",
            "The running loss is:\n",
            "6.9152334509417415\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.26597051734391314\n",
            "The running loss is:\n",
            "3.967709585092962\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.15260421481126776\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174106-yrjbk6sj/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174106-yrjbk6sj/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "3.5715523255057633\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.13736739713483706\n",
            "Data saved to: \n",
            "28_May_202005_41PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_41PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['yrjbk6sj']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  814.696899\n",
            "66  2020-04-22  sub_region  ...             66  825.249207\n",
            "67  2020-04-23  sub_region  ...             67  843.944824\n",
            "68  2020-04-24  sub_region  ...             68  816.804077\n",
            "69  2020-04-25  sub_region  ...             69  796.358887\n",
            "70  2020-04-26  sub_region  ...             70  799.779358\n",
            "71  2020-04-27  sub_region  ...             71  801.697021\n",
            "72  2020-04-28  sub_region  ...             72  800.930298\n",
            "73  2020-04-29  sub_region  ...             73  815.445740\n",
            "74  2020-04-30  sub_region  ...             74  822.305115\n",
            "\n",
            "[21 rows x 28 columns]"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174106-yrjbk6sj/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174106-yrjbk6sj/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174106-yrjbk6sj/wandb-history.jsonl\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174106-yrjbk6sj/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174106-yrjbk6sj/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174106-yrjbk6sj/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174106-yrjbk6sj/media/plotly/test_plot_20_d82f34c9.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174106-yrjbk6sj/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174106-yrjbk6sj/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
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            "wandb: Agent Finished Run: yrjbk6sj \n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174106-yrjbk6sj/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174106-yrjbk6sj/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174106-yrjbk6sj/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174106-yrjbk6sj/media/plotly\n",
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            "INFO:wandb.wandb_agent:Cleaning up finished run: yrjbk6sj\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
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            "wandb: Agent Starting Run: xcgr1z21 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: xcgr1z21\n"
          ],
          "name": "stdout"
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          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/xcgr1z21\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/xcgr1z21</a><br/>\n",
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          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "18.905425552278757\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.7271317520107214\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174122-xcgr1z21/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174122-xcgr1z21/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174122-xcgr1z21/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174122-xcgr1z21/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174122-xcgr1z21/media/graph/graph_0_summary_b755edb4.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174122-xcgr1z21/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174122-xcgr1z21/wandb-summary.json\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.324342384934425\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.6278593224974779\n",
            "1\n",
            "The running loss is:\n",
            "15.281524695456028\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.5877509498252318\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174122-xcgr1z21/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174122-xcgr1z21/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.686566263437271\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.41102177936297196\n",
            "1\n",
            "The running loss is:\n",
            "6.059355471283197\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.2330521335108922\n",
            "The running loss is:\n",
            "4.697019984945655\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.1806546148056021\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174122-xcgr1z21/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174122-xcgr1z21/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.977002476342022\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.26834624909007776\n",
            "1\n",
            "The running loss is:\n",
            "5.771942357998341\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.2219977829999362\n",
            "The running loss is:\n",
            "4.5645317947492\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.17555891518266156\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174122-xcgr1z21/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174122-xcgr1z21/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.686972144991159\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.21872969788427538\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_41PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_41PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['xcgr1z21']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174122-xcgr1z21/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174122-xcgr1z21/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174122-xcgr1z21/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  736.851746\n",
            "66  2020-04-22  sub_region  ...             66  772.893066\n",
            "67  2020-04-23  sub_region  ...             67  762.027588\n",
            "68  2020-04-24  sub_region  ...             68  752.332520\n",
            "69  2020-04-25  sub_region  ...             69  736.720947\n",
            "70  2020-04-26  sub_region  ...             70  741.464417\n",
            "71  2020-04-27  sub_region  ...             71  733.462769\n",
            "72  2020-04-28  sub_region  ...             72  760.639526\n",
            "73  2020-04-29  sub_region  ...             73  746.799805\n",
            "74  2020-04-30  sub_region  ...             74  770.807861\n",
            "\n",
            "[21 rows x 28 columns]\n"
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          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174122-xcgr1z21/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174122-xcgr1z21/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174122-xcgr1z21/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174122-xcgr1z21/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174122-xcgr1z21/media/plotly/test_plot_20_556d85cf.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174122-xcgr1z21/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174122-xcgr1z21/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: xcgr1z21 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174122-xcgr1z21/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174122-xcgr1z21/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174122-xcgr1z21/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174122-xcgr1z21/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174122-xcgr1z21/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174122-xcgr1z21/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: xcgr1z21\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 8a3n97xz with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 8a3n97xz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/8a3n97xz\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/8a3n97xz</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/8a3n97xz\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjhhM245N3h6OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "16.948153391480446\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.6518520535184786\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174139-8a3n97xz/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174139-8a3n97xz/media/graph/graph_0_summary_8afade4d.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174139-8a3n97xz/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174139-8a3n97xz/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174139-8a3n97xz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174139-8a3n97xz/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174139-8a3n97xz/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174139-8a3n97xz/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174139-8a3n97xz/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.85379159078002\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.5328381381069238\n",
            "The running loss is:\n",
            "9.64835274219513\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.37109049008442807\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174139-8a3n97xz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174139-8a3n97xz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.4732926171273\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.3258958698895115\n",
            "The running loss is:\n",
            "5.7244476936757565\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.22017106514137524\n",
            "The running loss is:\n",
            "6.5513798370957375\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.25197614758060527\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174139-8a3n97xz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174139-8a3n97xz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.37942417897284\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.3222855453451092\n",
            "The running loss is:\n",
            "4.463485406711698\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.1716725156427576\n",
            "1\n",
            "The running loss is:\n",
            "4.722438246943057\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.18163224026704064\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174139-8a3n97xz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174139-8a3n97xz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.340588949620724\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.1669457288315663\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_41PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_41PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['8a3n97xz']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174139-8a3n97xz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174139-8a3n97xz/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174139-8a3n97xz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  851.006592\n",
            "66  2020-04-22  sub_region  ...             66  861.678711\n",
            "67  2020-04-23  sub_region  ...             67  870.574524\n",
            "68  2020-04-24  sub_region  ...             68  854.424133\n",
            "69  2020-04-25  sub_region  ...             69  835.877502\n",
            "70  2020-04-26  sub_region  ...             70  840.583374\n",
            "71  2020-04-27  sub_region  ...             71  839.167969\n",
            "72  2020-04-28  sub_region  ...             72  850.335999\n",
            "73  2020-04-29  sub_region  ...             73  848.938354\n",
            "74  2020-04-30  sub_region  ...             74  859.935547\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174139-8a3n97xz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174139-8a3n97xz/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174139-8a3n97xz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174139-8a3n97xz/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174139-8a3n97xz/media/plotly/test_plot_20_0f4d1d99.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174139-8a3n97xz/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174139-8a3n97xz/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 8a3n97xz \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174139-8a3n97xz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174139-8a3n97xz/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174139-8a3n97xz/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174139-8a3n97xz/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174139-8a3n97xz/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174139-8a3n97xz/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 8a3n97xz\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 13w5kfa8 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 13w5kfa8\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/13w5kfa8\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/13w5kfa8</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/13w5kfa8\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjEzdzVrZmE4OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "16.634709760546684\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.6397965292517955\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174155-13w5kfa8/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174155-13w5kfa8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174155-13w5kfa8/media/graph/graph_0_summary_96db89bb.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174155-13w5kfa8/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174155-13w5kfa8/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174155-13w5kfa8/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174155-13w5kfa8/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174155-13w5kfa8/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174155-13w5kfa8/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.594469293951988\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.5613257420750765\n",
            "The running loss is:\n",
            "9.017393708229065\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.3468228349318871\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174155-13w5kfa8/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174155-13w5kfa8/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.2338755913078785\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.27822598428107226\n",
            "The running loss is:\n",
            "4.978128891438246\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.19146649582454792\n",
            "1\n",
            "The running loss is:\n",
            "5.498803533613682\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.21149244360052621\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174155-13w5kfa8/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174155-13w5kfa8/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.14033218100667\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.27462816080794883\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_41PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_41PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174155-13w5kfa8/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174155-13w5kfa8/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174155-13w5kfa8/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
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            "65  2020-04-21  sub_region  ...             65  736.526611\n",
            "66  2020-04-22  sub_region  ...             66  758.064575\n",
            "67  2020-04-23  sub_region  ...             67  751.773193\n",
            "68  2020-04-24  sub_region  ...             68  746.766174\n",
            "69  2020-04-25  sub_region  ...             69  738.920166\n",
            "70  2020-04-26  sub_region  ...             70  743.997314\n",
            "71  2020-04-27  sub_region  ...             71  741.321045\n",
            "72  2020-04-28  sub_region  ...             72  750.091064\n",
            "73  2020-04-29  sub_region  ...             73  743.901367\n",
            "74  2020-04-30  sub_region  ...             74  754.618958\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['13w5kfa8']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174155-13w5kfa8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174155-13w5kfa8/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174155-13w5kfa8/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174155-13w5kfa8/wandb-summary.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174155-13w5kfa8/media/plotly/test_plot_14_251cab71.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174155-13w5kfa8/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174155-13w5kfa8/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 13w5kfa8 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174155-13w5kfa8/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174155-13w5kfa8/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174155-13w5kfa8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174155-13w5kfa8/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174155-13w5kfa8/media/plotly/test_plot_all_15_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174155-13w5kfa8/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 13w5kfa8\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: xmks4586 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: xmks4586\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/xmks4586\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/xmks4586</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/xmks4586\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174208-xmks4586/wandb-metadata.json\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnhta3M0NTg2OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "14.528097435832024\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.581123897433281\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174208-xmks4586/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.321749061346054\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.5728699624538421\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174208-xmks4586/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174208-xmks4586/media/graph/graph_0_summary_a2ecbda0.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174208-xmks4586/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174208-xmks4586/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174208-xmks4586/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174208-xmks4586/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174208-xmks4586/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.53690830618143\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.3414763322472572\n",
            "1\n",
            "The running loss is:\n",
            "3.877267986536026\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.15509071946144104\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174208-xmks4586/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174208-xmks4586/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "3.9804319692775607\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.15921727877110242\n",
            "1\n",
            "The running loss is:\n",
            "4.088011069223285\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.16352044276893138\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['xmks4586']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "3.642200954724103\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.14568803818896414\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174208-xmks4586/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174208-xmks4586/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.692892727442086\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.18771570909768343\n",
            "2\n",
            "The running loss is:\n",
            "3.2474756240844727\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.1298990249633789\n",
            "The running loss is:\n",
            "3.19531544810161\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.1278126179240644\n",
            "Data saved to: \n",
            "28_May_202005_42PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174208-xmks4586/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174208-xmks4586/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_42PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  711.413086\n",
            "66  2020-04-22  sub_region  ...             66  714.489502\n",
            "67  2020-04-23  sub_region  ...             67  749.420166\n",
            "68  2020-04-24  sub_region  ...             68  730.427063\n",
            "69  2020-04-25  sub_region  ...             69  707.012146\n",
            "70  2020-04-26  sub_region  ...             70  700.846252\n",
            "71  2020-04-27  sub_region  ...             71  701.634644\n",
            "72  2020-04-28  sub_region  ...             72  702.439697\n",
            "73  2020-04-29  sub_region  ...             73  710.740356\n",
            "74  2020-04-30  sub_region  ...             74  736.382812\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174208-xmks4586/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174208-xmks4586/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: xmks4586 \n",
            "\n"
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174208-xmks4586/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174208-xmks4586/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174208-xmks4586/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174208-xmks4586/media/plotly/test_plot_20_b4b65440.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174208-xmks4586/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174208-xmks4586/media/plotly\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174208-xmks4586/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: xmks4586\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
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          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: yq5x8xdo with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: yq5x8xdo\n"
          ],
          "name": "stdout"
        },
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          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/yq5x8xdo\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/yq5x8xdo</a><br/>\n",
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          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174225-yq5x8xdo/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174225-yq5x8xdo/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174225-yq5x8xdo/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174225-yq5x8xdo/media/graph/graph_0_summary_ec8df8ad.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174225-yq5x8xdo/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174225-yq5x8xdo/wandb-summary.json\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.583468616008759\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.5833387446403503\n",
            "The running loss is:\n",
            "11.234198205173016\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.4493679282069206\n",
            "1\n",
            "The running loss is:\n",
            "6.8342317044734955\n",
            "The number of items in train is: \n",
            "25\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174225-yq5x8xdo/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174225-yq5x8xdo/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The loss for epoch 2\n",
            "0.2733692681789398\n",
            "2\n",
            "The running loss is:\n",
            "4.914771635085344\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.19659086540341378\n",
            "The running loss is:\n",
            "6.097113943658769\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.24388455774635076\n",
            "1\n",
            "The running loss is:\n",
            "5.855831284075975\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174225-yq5x8xdo/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0.23423325136303902\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174225-yq5x8xdo/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "2\n",
            "The running loss is:\n",
            "4.823610462248325\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.19294441848993302\n",
            "The running loss is:\n",
            "5.116255223751068\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.2046502089500427\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174225-yq5x8xdo/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174225-yq5x8xdo/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "3.9354080138728023\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.1574163205549121\n",
            "2\n",
            "The running loss is:\n",
            "3.635712749324739\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.14542850997298956\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_42PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.wandb_agent:Running runs: ['yq5x8xdo']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_42PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174225-yq5x8xdo/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174225-yq5x8xdo/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174225-yq5x8xdo/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  742.793396\n",
            "66  2020-04-22  sub_region  ...             66  736.652344\n",
            "67  2020-04-23  sub_region  ...             67  754.830688\n",
            "68  2020-04-24  sub_region  ...             68  749.173950\n",
            "69  2020-04-25  sub_region  ...             69  738.829346\n",
            "70  2020-04-26  sub_region  ...             70  734.617004\n",
            "71  2020-04-27  sub_region  ...             71  732.794556\n",
            "72  2020-04-28  sub_region  ...             72  736.974976\n",
            "73  2020-04-29  sub_region  ...             73  737.531616\n",
            "74  2020-04-30  sub_region  ...             74  753.737549\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174225-yq5x8xdo/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174225-yq5x8xdo/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174225-yq5x8xdo/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174225-yq5x8xdo/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174225-yq5x8xdo/media/plotly/test_plot_20_71421788.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174225-yq5x8xdo/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174225-yq5x8xdo/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: yq5x8xdo \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174225-yq5x8xdo/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174225-yq5x8xdo/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174225-yq5x8xdo/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174225-yq5x8xdo/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174225-yq5x8xdo/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174225-yq5x8xdo/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: yq5x8xdo\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 76vrqty5 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 76vrqty5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/76vrqty5\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/76vrqty5</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/76vrqty5\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174241-76vrqty5/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Running runs: ['76vrqty5']\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174241-76vrqty5/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.349453534625354\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.4573871679490872\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174241-76vrqty5/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174241-76vrqty5/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174241-76vrqty5/media/graph/graph_0_summary_929ab4e3.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174241-76vrqty5/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174241-76vrqty5/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174241-76vrqty5/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "30.535183822968975\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.130932734184036\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174241-76vrqty5/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174241-76vrqty5/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.304419979453087\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.6779414807204847\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174241-76vrqty5/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174241-76vrqty5/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.870923589682207\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.4026267996178595\n",
            "2\n",
            "The running loss is:\n",
            "9.898105231113732\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.36659649004124933\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_42PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174241-76vrqty5/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174241-76vrqty5/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_42PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  502.529541\n",
            "66  2020-04-22  sub_region  ...             66  492.594849\n",
            "67  2020-04-23  sub_region  ...             67  496.524628\n",
            "68  2020-04-24  sub_region  ...             68  495.569824\n",
            "69  2020-04-25  sub_region  ...             69  495.434570\n",
            "70  2020-04-26  sub_region  ...             70  493.173859\n",
            "71  2020-04-27  sub_region  ...             71  493.890808\n",
            "72  2020-04-28  sub_region  ...             72  491.183136\n",
            "73  2020-04-29  sub_region  ...             73  497.434570\n",
            "74  2020-04-30  sub_region  ...             74  489.901245\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174241-76vrqty5/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174241-76vrqty5/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 76vrqty5 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174241-76vrqty5/wandb-metadata.json\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174241-76vrqty5/media/plotly/test_plot_10_c557f1bd.plotly.json\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174241-76vrqty5/media/plotly\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174241-76vrqty5/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 76vrqty5\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
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          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: vso6t8md with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: vso6t8md\n"
          ],
          "name": "stdout"
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          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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          "name": "stderr"
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        {
          "output_type": "display_data",
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/vso6t8md\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/vso6t8md</a><br/>\n",
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            "DEBUG:wandb.meta:probe for git information\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/vso6t8md\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
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            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
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        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174305-vso6t8md/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174305-vso6t8md/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174305-vso6t8md/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174305-vso6t8md/media/graph/graph_0_summary_0ea44e09.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174305-vso6t8md/requirements.txt\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.00125639885664\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.4444909777354311\n",
            "The running loss is:\n",
            "27.564760353183374\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.0209170501179028\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174305-vso6t8md/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174305-vso6t8md/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.1493139838567\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7092338512539519\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174305-vso6t8md/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174305-vso6t8md/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.482985021779314\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.3512216674733079\n",
            "The running loss is:\n",
            "8.379846808500588\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.31036469661113286\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174305-vso6t8md/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174305-vso6t8md/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.564052293615532\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.24311304791168636\n",
            "The running loss is:\n",
            "7.043116731874761\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.2608561752546208\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174305-vso6t8md/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174305-vso6t8md/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.5104550529504195\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.2411279649240896\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174305-vso6t8md/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174305-vso6t8md/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.045392430853099\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.2609404604019666\n",
            "The running loss is:\n",
            "6.926817309111357\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.25654878922634655\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_43PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174305-vso6t8md/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174305-vso6t8md/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174305-vso6t8md/wandb-metadata.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_43PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  778.043945\n",
            "66  2020-04-22  sub_region  ...             66  783.455933\n",
            "67  2020-04-23  sub_region  ...             67  766.487671\n",
            "68  2020-04-24  sub_region  ...             68  773.625916\n",
            "69  2020-04-25  sub_region  ...             69  782.676147\n",
            "70  2020-04-26  sub_region  ...             70  784.909546\n",
            "71  2020-04-27  sub_region  ...             71  784.748901\n",
            "72  2020-04-28  sub_region  ...             72  778.614258\n",
            "73  2020-04-29  sub_region  ...             73  782.117676\n",
            "74  2020-04-30  sub_region  ...             74  761.960938\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174305-vso6t8md/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174305-vso6t8md/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174305-vso6t8md/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174305-vso6t8md/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174305-vso6t8md/media/plotly/test_plot_20_647b6c30.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174305-vso6t8md/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174305-vso6t8md/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: vso6t8md \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174305-vso6t8md/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174305-vso6t8md/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174305-vso6t8md/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174305-vso6t8md/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174305-vso6t8md/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174305-vso6t8md/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: vso6t8md\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 9dwjgh02 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 9dwjgh02\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/9dwjgh02\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/9dwjgh02</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/9dwjgh02\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174334-9dwjgh02/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Running runs: ['9dwjgh02']\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174334-9dwjgh02/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.362042212858796\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174334-9dwjgh02/requirements.txt\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The number of items in train is: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174334-9dwjgh02/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "27\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174334-9dwjgh02/media/graph/graph_0_summary_3bf2505d.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174334-9dwjgh02/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The loss for epoch 0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174334-9dwjgh02/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0.5689645264021777\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174334-9dwjgh02/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.604257106781006\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.6890465595104076\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174334-9dwjgh02/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174334-9dwjgh02/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.387704892084002\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7180631441512594\n",
            "The running loss is:\n",
            "17.115502156317234\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174334-9dwjgh02/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The number of items in train is: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174334-9dwjgh02/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "27\n",
            "The loss for epoch 3\n",
            "0.6339074872710087\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Network error resolved after 0:00:12.859720, resuming normal operation.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.399747041985393\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.3481387793327923\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174334-9dwjgh02/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174334-9dwjgh02/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.062234118580818\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.2986012636511414\n",
            "The running loss is:\n",
            "7.508644045330584\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.2780979276048364\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174334-9dwjgh02/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174334-9dwjgh02/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.624856340698898\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.2824020866925518\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174334-9dwjgh02/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174334-9dwjgh02/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.4580579325556755\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.27622436787243243\n",
            "The running loss is:\n",
            "6.958448266610503\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.25772030617075936\n",
            "Data saved to: \n",
            "28_May_202005_43PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174334-9dwjgh02/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174334-9dwjgh02/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174334-9dwjgh02/wandb-metadata.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_43PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  772.555298\n",
            "66  2020-04-22  sub_region  ...             66  766.406433\n",
            "67  2020-04-23  sub_region  ...             67  773.479492\n",
            "68  2020-04-24  sub_region  ...             68  770.044922\n",
            "69  2020-04-25  sub_region  ...             69  772.980347\n",
            "70  2020-04-26  sub_region  ...             70  772.595215\n",
            "71  2020-04-27  sub_region  ...             71  772.935425\n",
            "72  2020-04-28  sub_region  ...             72  770.757202\n",
            "73  2020-04-29  sub_region  ...             73  772.154602\n",
            "74  2020-04-30  sub_region  ...             74  771.334900\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174334-9dwjgh02/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174334-9dwjgh02/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174334-9dwjgh02/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174334-9dwjgh02/wandb-summary.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174334-9dwjgh02/media/plotly/test_plot_20_cf3ed598.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174334-9dwjgh02/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174334-9dwjgh02/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 9dwjgh02 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174334-9dwjgh02/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174334-9dwjgh02/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174334-9dwjgh02/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174334-9dwjgh02/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174334-9dwjgh02/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174334-9dwjgh02/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 9dwjgh02\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: xnsxnzq6 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: xnsxnzq6\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/xnsxnzq6\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/xnsxnzq6</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/xnsxnzq6\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnhuc3huenE2OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174400-xnsxnzq6/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174400-xnsxnzq6/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174400-xnsxnzq6/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174400-xnsxnzq6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174400-xnsxnzq6/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174400-xnsxnzq6/media/graph/graph_0_summary_7969e239.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174400-xnsxnzq6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174400-xnsxnzq6/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174400-xnsxnzq6/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.227613762952387\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.5639856949241625\n",
            "The running loss is:\n",
            "17.898133425042033\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.6628938305571124\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174400-xnsxnzq6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174400-xnsxnzq6/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.654380347579718\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.5797918647251747\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174400-xnsxnzq6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174400-xnsxnzq6/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.89348728954792\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.3664254551684415\n",
            "1\n",
            "The running loss is:\n",
            "8.431944162817672\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.31229422825250636\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174400-xnsxnzq6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174400-xnsxnzq6/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.609166414942592\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.31885801536824415\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['xnsxnzq6']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.016866168007255\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.3339580062224909\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174400-xnsxnzq6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174400-xnsxnzq6/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.0558643322438\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.29836534563865924\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174400-xnsxnzq6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174400-xnsxnzq6/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.685117572546005\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.3216710212054076\n",
            "1\n",
            "The running loss is:\n",
            "6.745234100148082\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.2498234851906697\n",
            "Data saved to: \n",
            "28_May_202005_44PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174400-xnsxnzq6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174400-xnsxnzq6/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_44PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  783.742432\n",
            "66  2020-04-22  sub_region  ...             66  782.695801\n",
            "67  2020-04-23  sub_region  ...             67  783.955322\n",
            "68  2020-04-24  sub_region  ...             68  783.325439\n",
            "69  2020-04-25  sub_region  ...             69  783.187744\n",
            "70  2020-04-26  sub_region  ...             70  783.111938\n",
            "71  2020-04-27  sub_region  ...             71  783.345886\n",
            "72  2020-04-28  sub_region  ...             72  782.962280\n",
            "73  2020-04-29  sub_region  ...             73  782.751831\n",
            "74  2020-04-30  sub_region  ...             74  783.127808\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174400-xnsxnzq6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174400-xnsxnzq6/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: xnsxnzq6 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174400-xnsxnzq6/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174400-xnsxnzq6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174400-xnsxnzq6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174400-xnsxnzq6/media/plotly/test_plot_20_86f33ef2.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174400-xnsxnzq6/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174400-xnsxnzq6/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174400-xnsxnzq6/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174400-xnsxnzq6/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: xnsxnzq6\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: tr5rhjwy with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: tr5rhjwy\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/tr5rhjwy\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/tr5rhjwy</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/tr5rhjwy\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnRyNXJoand5OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174416-tr5rhjwy/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174416-tr5rhjwy/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174416-tr5rhjwy/media/graph/graph_0_summary_ff3d77af.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174416-tr5rhjwy/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174416-tr5rhjwy/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174416-tr5rhjwy/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174416-tr5rhjwy/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174416-tr5rhjwy/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174416-tr5rhjwy/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.012039236724377\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.8081553552586299\n",
            "The running loss is:\n",
            "16.833910040557384\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.6474580784829763\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174416-tr5rhjwy/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174416-tr5rhjwy/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.23337048664689\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.6243604033325727\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174416-tr5rhjwy/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174416-tr5rhjwy/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.538650223985314\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.5976403932302043\n",
            "1\n",
            "The running loss is:\n",
            "15.044064596295357\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.5786178690882829\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174416-tr5rhjwy/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174416-tr5rhjwy/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.831363804638386\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.4550524540245533\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['tr5rhjwy']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.703582746908069\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.2962916441118488\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174416-tr5rhjwy/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174416-tr5rhjwy/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.853043738752604\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.3405016822597155\n",
            "2\n",
            "The running loss is:\n",
            "5.49346605874598\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174416-tr5rhjwy/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0.21128715610561463\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174416-tr5rhjwy/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.049623145721853\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.23267781329699433\n",
            "Data saved to: \n",
            "28_May_202005_44PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174416-tr5rhjwy/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174416-tr5rhjwy/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_44PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  793.348389\n",
            "66  2020-04-22  sub_region  ...             66  793.042908\n",
            "67  2020-04-23  sub_region  ...             67  790.546631\n",
            "68  2020-04-24  sub_region  ...             68  791.214233\n",
            "69  2020-04-25  sub_region  ...             69  792.006836\n",
            "70  2020-04-26  sub_region  ...             70  792.608032\n",
            "71  2020-04-27  sub_region  ...             71  792.963989\n",
            "72  2020-04-28  sub_region  ...             72  789.634521\n",
            "73  2020-04-29  sub_region  ...             73  793.287476\n",
            "74  2020-04-30  sub_region  ...             74  785.882202\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174416-tr5rhjwy/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174416-tr5rhjwy/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: tr5rhjwy \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174416-tr5rhjwy/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174416-tr5rhjwy/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174416-tr5rhjwy/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174416-tr5rhjwy/media/plotly/test_plot_20_ef160644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174416-tr5rhjwy/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174416-tr5rhjwy/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174416-tr5rhjwy/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174416-tr5rhjwy/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: tr5rhjwy\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 98gbz6d6 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 98gbz6d6\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/98gbz6d6\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/98gbz6d6</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/98gbz6d6\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjk4Z2J6NmQ2OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174434-98gbz6d6/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174434-98gbz6d6/media/graph/graph_0_summary_6698a735.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174434-98gbz6d6/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174434-98gbz6d6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174434-98gbz6d6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174434-98gbz6d6/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174434-98gbz6d6/wandb-metadata.json\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174434-98gbz6d6/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.714700505137444\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.7967192501975939\n",
            "The running loss is:\n",
            "16.891354955732822\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.6496674982974162\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174434-98gbz6d6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174434-98gbz6d6/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.139072712510824\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.620733565865801\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174434-98gbz6d6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174434-98gbz6d6/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.367559241130948\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.5910599708127288\n",
            "2\n",
            "The running loss is:\n",
            "14.148938469588757\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.5441899411380291\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_44PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174434-98gbz6d6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174434-98gbz6d6/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_44PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['98gbz6d6']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  440.134735\n",
            "66  2020-04-22  sub_region  ...             66  439.800903\n",
            "67  2020-04-23  sub_region  ...             67  443.283325\n",
            "68  2020-04-24  sub_region  ...             68  442.842438\n",
            "69  2020-04-25  sub_region  ...             69  440.653381\n",
            "70  2020-04-26  sub_region  ...             70  441.242767\n",
            "71  2020-04-27  sub_region  ...             71  440.577118\n",
            "72  2020-04-28  sub_region  ...             72  442.682800\n",
            "73  2020-04-29  sub_region  ...             73  441.382904\n",
            "74  2020-04-30  sub_region  ...             74  445.453522\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174434-98gbz6d6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174434-98gbz6d6/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174434-98gbz6d6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174434-98gbz6d6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174434-98gbz6d6/media/plotly/test_plot_10_0d49373a.plotly.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174434-98gbz6d6/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174434-98gbz6d6/media/plotly\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 98gbz6d6 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174434-98gbz6d6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174434-98gbz6d6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174434-98gbz6d6/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174434-98gbz6d6/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174434-98gbz6d6/media/plotly/test_plot_all_11_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174434-98gbz6d6/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 98gbz6d6\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: e13nqx3k with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: e13nqx3k\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/e13nqx3k\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/e13nqx3k</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/e13nqx3k\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmUxM25xeDNrOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174445-e13nqx3k/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "23.482627481222153\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.9031779800470059\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174445-e13nqx3k/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174445-e13nqx3k/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174445-e13nqx3k/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174445-e13nqx3k/media/graph/graph_0_summary_bcd87636.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174445-e13nqx3k/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174445-e13nqx3k/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174445-e13nqx3k/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174445-e13nqx3k/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.23513476550579\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.778274414057915\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174445-e13nqx3k/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174445-e13nqx3k/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.649361565709114\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.8326677525272737\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174445-e13nqx3k/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174445-e13nqx3k/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.995433270931244\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.615208971958894\n",
            "1\n",
            "The running loss is:\n",
            "12.781472019851208\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.4915950776865849\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174445-e13nqx3k/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174445-e13nqx3k/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.850842893123627\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.3404170343509087\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['e13nqx3k']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.8902468085289\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.26500949263572693\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174445-e13nqx3k/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174445-e13nqx3k/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.579151637852192\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.32996737068662274\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174445-e13nqx3k/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174445-e13nqx3k/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.014725340530276\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.23133559002039525\n",
            "The running loss is:\n",
            "4.920796798542142\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.18926141532854393\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_44PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174445-e13nqx3k/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174445-e13nqx3k/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_44PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  784.844604\n",
            "66  2020-04-22  sub_region  ...             66  785.061829\n",
            "67  2020-04-23  sub_region  ...             67  785.951660\n",
            "68  2020-04-24  sub_region  ...             68  785.422729\n",
            "69  2020-04-25  sub_region  ...             69  784.747925\n",
            "70  2020-04-26  sub_region  ...             70  784.533691\n",
            "71  2020-04-27  sub_region  ...             71  784.408447\n",
            "72  2020-04-28  sub_region  ...             72  785.171936\n",
            "73  2020-04-29  sub_region  ...             73  784.649170\n",
            "74  2020-04-30  sub_region  ...             74  785.943665\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174445-e13nqx3k/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174445-e13nqx3k/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: e13nqx3k \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174445-e13nqx3k/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174445-e13nqx3k/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174445-e13nqx3k/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174445-e13nqx3k/media/plotly/test_plot_20_bf33d510.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174445-e13nqx3k/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174445-e13nqx3k/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174445-e13nqx3k/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174445-e13nqx3k/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: e13nqx3k\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: i222q13p with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: i222q13p\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/i222q13p\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/i222q13p</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/i222q13p\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174501-i222q13p/wandb-metadata.json\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmkyMjJxMTNwOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174501-i222q13p/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174501-i222q13p/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174501-i222q13p/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174501-i222q13p/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174501-i222q13p/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174501-i222q13p/media/graph/graph_0_summary_55e43b9c.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174501-i222q13p/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174501-i222q13p/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "23.31422958523035\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.896701137893475\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['i222q13p']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174501-i222q13p/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174501-i222q13p/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.628748193383217\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.7549518535916622\n",
            "1\n",
            "The running loss is:\n",
            "22.03840383887291\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.8476309168797272\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174501-i222q13p/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174501-i222q13p/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.539160393178463\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.5976600151222485\n",
            "1\n",
            "The running loss is:\n",
            "12.818177118897438\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.4930068122652861\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174501-i222q13p/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174501-i222q13p/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.63734956085682\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.4091288292637238\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174501-i222q13p/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174501-i222q13p/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.949803344905376\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.2673001286502068\n",
            "The running loss is:\n",
            "7.64347056671977\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.29397963718152964\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174501-i222q13p/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174501-i222q13p/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.507175385951996\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.4425836686904614\n",
            "The running loss is:\n",
            "5.667488571256399\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.21798032966370767\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174501-i222q13p/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174501-i222q13p/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "28_May_202005_45PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_45PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  749.537842\n",
            "66  2020-04-22  sub_region  ...             66  753.229736\n",
            "67  2020-04-23  sub_region  ...             67  753.515015\n",
            "68  2020-04-24  sub_region  ...             68  753.205444\n",
            "69  2020-04-25  sub_region  ...             69  751.122925\n",
            "70  2020-04-26  sub_region  ...             70  750.473755\n",
            "71  2020-04-27  sub_region  ...             71  748.162842\n",
            "72  2020-04-28  sub_region  ...             72  752.245361\n",
            "73  2020-04-29  sub_region  ...             73  750.474243\n",
            "74  2020-04-30  sub_region  ...             74  754.320679\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174501-i222q13p/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174501-i222q13p/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: i222q13p \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174501-i222q13p/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174501-i222q13p/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174501-i222q13p/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174501-i222q13p/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174501-i222q13p/media/plotly/test_plot_20_67e12e5e.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174501-i222q13p/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174501-i222q13p/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174501-i222q13p/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: i222q13p\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
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            "wandb: Agent Starting Run: f6en72ry with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: f6en72ry\n"
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          "name": "stdout"
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          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/f6en72ry\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/f6en72ry</a><br/>\n",
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          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174523-f6en72ry/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174523-f6en72ry/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174523-f6en72ry/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174523-f6en72ry/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174523-f6en72ry/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174523-f6en72ry/requirements.txt\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174523-f6en72ry/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.830005079507828\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.7132002031803131\n",
            "The running loss is:\n",
            "16.9639795422554\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.6785591816902161\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174523-f6en72ry/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174523-f6en72ry/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.417406290769577\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.776696251630783\n",
            "The running loss is:\n",
            "13.737709395587444\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.5495083758234978\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174523-f6en72ry/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174523-f6en72ry/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.103170596063137\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.5241268238425255\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174523-f6en72ry/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174523-f6en72ry/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.996175795793533\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.3998470318317413\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_45PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_45PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['f6en72ry']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174523-f6en72ry/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174523-f6en72ry/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174523-f6en72ry/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  417.858063\n",
            "66  2020-04-22  sub_region  ...             66  419.026245\n",
            "67  2020-04-23  sub_region  ...             67  420.017090\n",
            "68  2020-04-24  sub_region  ...             68  420.572571\n",
            "69  2020-04-25  sub_region  ...             69  419.899597\n",
            "70  2020-04-26  sub_region  ...             70  419.641113\n",
            "71  2020-04-27  sub_region  ...             71  419.378143\n",
            "72  2020-04-28  sub_region  ...             72  419.695251\n",
            "73  2020-04-29  sub_region  ...             73  419.175232\n",
            "74  2020-04-30  sub_region  ...             74  421.516815\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174523-f6en72ry/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174523-f6en72ry/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174523-f6en72ry/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174523-f6en72ry/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174523-f6en72ry/media/plotly/test_plot_12_d17e53a2.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174523-f6en72ry/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174523-f6en72ry/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: f6en72ry \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174523-f6en72ry/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174523-f6en72ry/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174523-f6en72ry/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174523-f6en72ry/media/plotly/test_plot_all_13_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174523-f6en72ry/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174523-f6en72ry/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: f6en72ry\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 3et7fdzd with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 3et7fdzd\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/3et7fdzd\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/3et7fdzd</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/3et7fdzd\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjNldDdmZHpkOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174539-3et7fdzd/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174539-3et7fdzd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174539-3et7fdzd/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174539-3et7fdzd/media/graph/graph_0_summary_29229bcb.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174539-3et7fdzd/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174539-3et7fdzd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174539-3et7fdzd/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174539-3et7fdzd/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174539-3et7fdzd/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.741086795926094\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.7096434718370438\n",
            "The running loss is:\n",
            "16.730679839849472\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.6692271935939789\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174539-3et7fdzd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174539-3et7fdzd/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.939407482743263\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.7975762993097305\n",
            "The running loss is:\n",
            "13.255669929087162\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.5302267971634865\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174539-3et7fdzd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174539-3et7fdzd/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "11.027616888284683\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.4411046755313873\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174539-3et7fdzd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174539-3et7fdzd/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.718288138508797\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.26873152554035185\n",
            "The running loss is:\n",
            "7.313029149547219\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.29252116598188876\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['3et7fdzd']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174539-3et7fdzd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174539-3et7fdzd/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.773601979017258\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.5109440791606903\n",
            "The running loss is:\n",
            "6.80379575304687\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.2721518301218748\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174539-3et7fdzd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174539-3et7fdzd/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.739170776680112\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.18956683106720448\n",
            "2\n",
            "Data saved to: \n",
            "28_May_202005_45PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_45PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174539-3et7fdzd/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174539-3et7fdzd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174539-3et7fdzd/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  770.853760\n",
            "66  2020-04-22  sub_region  ...             66  769.976440\n",
            "67  2020-04-23  sub_region  ...             67  771.220154\n",
            "68  2020-04-24  sub_region  ...             68  770.911987\n",
            "69  2020-04-25  sub_region  ...             69  770.517700\n",
            "70  2020-04-26  sub_region  ...             70  770.186768\n",
            "71  2020-04-27  sub_region  ...             71  770.193970\n",
            "72  2020-04-28  sub_region  ...             72  770.157593\n",
            "73  2020-04-29  sub_region  ...             73  770.232788\n",
            "74  2020-04-30  sub_region  ...             74  770.960083\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174539-3et7fdzd/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174539-3et7fdzd/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174539-3et7fdzd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174539-3et7fdzd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174539-3et7fdzd/media/plotly/test_plot_20_57ec849c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174539-3et7fdzd/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174539-3et7fdzd/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
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            "wandb: Agent Finished Run: 3et7fdzd \n",
            "\n"
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174539-3et7fdzd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174539-3et7fdzd/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174539-3et7fdzd/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174539-3et7fdzd/media/plotly\n",
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            "INFO:wandb.wandb_agent:Cleaning up finished run: 3et7fdzd\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
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          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: dqyud2we with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: dqyud2we\n"
          ],
          "name": "stdout"
        },
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          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/dqyud2we\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/dqyud2we</a><br/>\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/dqyud2we\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmRxeXVkMndlOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
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        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174555-dqyud2we/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174555-dqyud2we/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174555-dqyud2we/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174555-dqyud2we/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174555-dqyud2we/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174555-dqyud2we/media/graph/graph_0_summary_b022e570.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174555-dqyud2we/requirements.txt\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174555-dqyud2we/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.64892402663827\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.5055157046903063\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174555-dqyud2we/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174555-dqyud2we/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "37.692751033231616\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.3960278160456154\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174555-dqyud2we/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174555-dqyud2we/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.47567129507661\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7583581961139485\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174555-dqyud2we/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174555-dqyud2we/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.933722829446197\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.7753230677572666\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['dqyud2we']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174555-dqyud2we/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174555-dqyud2we/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.798208009451628\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.7703040003500603\n",
            "The running loss is:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174555-dqyud2we/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174555-dqyud2we/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "20.184429235756397\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.7475714531761629\n",
            "1\n",
            "The running loss is:\n",
            "20.411993622779846\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.755999763806661\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174555-dqyud2we/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174555-dqyud2we/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.94804622977972\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.7388165270288786\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_46PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174555-dqyud2we/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174555-dqyud2we/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_46PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  474.087341\n",
            "66  2020-04-22  sub_region  ...             66  474.107361\n",
            "67  2020-04-23  sub_region  ...             67  473.755005\n",
            "68  2020-04-24  sub_region  ...             68  474.003113\n",
            "69  2020-04-25  sub_region  ...             69  474.199402\n",
            "70  2020-04-26  sub_region  ...             70  474.221405\n",
            "71  2020-04-27  sub_region  ...             71  474.191833\n",
            "72  2020-04-28  sub_region  ...             72  473.996887\n",
            "73  2020-04-29  sub_region  ...             73  474.185181\n",
            "74  2020-04-30  sub_region  ...             74  473.697998\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174555-dqyud2we/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174555-dqyud2we/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: dqyud2we \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174555-dqyud2we/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174555-dqyud2we/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174555-dqyud2we/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174555-dqyud2we/media/plotly/test_plot_16_1f8d7214.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174555-dqyud2we/media/plotly/test_plot_all_17_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174555-dqyud2we/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174555-dqyud2we/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174555-dqyud2we/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: dqyud2we\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: k9df4wo5 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: k9df4wo5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/k9df4wo5\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/k9df4wo5</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/k9df4wo5\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOms5ZGY0d281OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/media/graph/graph_0_summary_882f1ddc.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.37478191871196\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.49536229328562814\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "37.45775743201375\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.3873243493338425\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.595209177583456\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7627855250956835\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.006036780774593\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.7780013622509109\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['k9df4wo5']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.73020259477198\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.7677852812878512\n",
            "The running loss is:\n",
            "20.24301341921091\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.7497412377485523\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "20.167157787829638\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.7469317699196162\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.895125970244408\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.6998194803794225\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_46PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_46PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/config.yaml\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  474.395355\n",
            "66  2020-04-22  sub_region  ...             66  474.474243\n",
            "67  2020-04-23  sub_region  ...             67  473.946503\n",
            "68  2020-04-24  sub_region  ...             68  474.251740\n",
            "69  2020-04-25  sub_region  ...             69  474.498688\n",
            "70  2020-04-26  sub_region  ...             70  474.531128\n",
            "71  2020-04-27  sub_region  ...             71  474.499969\n",
            "72  2020-04-28  sub_region  ...             72  474.257050\n",
            "73  2020-04-29  sub_region  ...             73  474.527222\n",
            "74  2020-04-30  sub_region  ...             74  473.818420\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/media/plotly/test_plot_16_c257944a.plotly.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: k9df4wo5 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/media/plotly/test_plot_all_17_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174612-k9df4wo5/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: k9df4wo5\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: vzgo44b3 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: vzgo44b3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/vzgo44b3\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/vzgo44b3</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/vzgo44b3\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnZ6Z280NGIzOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174628-vzgo44b3/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174628-vzgo44b3/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174628-vzgo44b3/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174628-vzgo44b3/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174628-vzgo44b3/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174628-vzgo44b3/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174628-vzgo44b3/media/graph/graph_0_summary_aa1f8d8a.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174628-vzgo44b3/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174628-vzgo44b3/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.137208757922053\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.5606373614045205\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174628-vzgo44b3/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174628-vzgo44b3/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "30.88287841156125\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.1438103115393057\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174628-vzgo44b3/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174628-vzgo44b3/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "25.371903955936432\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.9397001465161642\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174628-vzgo44b3/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174628-vzgo44b3/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.891498629003763\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.7737592084816208\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['vzgo44b3']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174628-vzgo44b3/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174628-vzgo44b3/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.58536821603775\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.7624210450384352\n",
            "1\n",
            "The running loss is:\n",
            "21.16692252457142\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.7839600935026452\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174628-vzgo44b3/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174628-vzgo44b3/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.775313809514046\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.7694560670190387\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_46PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174628-vzgo44b3/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174628-vzgo44b3/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_46PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174628-vzgo44b3/config.yaml\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  422.063049\n",
            "66  2020-04-22  sub_region  ...             66  422.065277\n",
            "67  2020-04-23  sub_region  ...             67  422.008148\n",
            "68  2020-04-24  sub_region  ...             68  422.044128\n",
            "69  2020-04-25  sub_region  ...             69  422.075989\n",
            "70  2020-04-26  sub_region  ...             70  422.085114\n",
            "71  2020-04-27  sub_region  ...             71  422.084900\n",
            "72  2020-04-28  sub_region  ...             72  422.078888\n",
            "73  2020-04-29  sub_region  ...             73  422.060028\n",
            "74  2020-04-30  sub_region  ...             74  422.037537\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174628-vzgo44b3/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174628-vzgo44b3/wandb-summary.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174628-vzgo44b3/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174628-vzgo44b3/media/plotly/test_plot_14_7a77c25e.plotly.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174628-vzgo44b3/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174628-vzgo44b3/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: vzgo44b3 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174628-vzgo44b3/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174628-vzgo44b3/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174628-vzgo44b3/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174628-vzgo44b3/media/plotly/test_plot_all_15_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174628-vzgo44b3/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174628-vzgo44b3/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: vzgo44b3\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: eplaca55 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: eplaca55\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/eplaca55\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/eplaca55</a><br/>\n",
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          "metadata": {
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        {
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/eplaca55\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/wandb-metadata.json\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmVwbGFjYTU1OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/media/graph/graph_0_summary_e671cd5d.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.071377269923687\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.5581991581453217\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "31.568736284971237\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.1692124549989347\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['eplaca55']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "25.198651179671288\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.9332833770248625\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.102718703448772\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.7815821742018064\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.57193885743618\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.7619236613865252\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.114420972764492\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.7820155915838701\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.067522056400776\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.7802785946815102\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_46PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_46PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  423.483063\n",
            "66  2020-04-22  sub_region  ...             66  423.511169\n",
            "67  2020-04-23  sub_region  ...             67  423.472900\n",
            "68  2020-04-24  sub_region  ...             68  423.498932\n",
            "69  2020-04-25  sub_region  ...             69  423.520294\n",
            "70  2020-04-26  sub_region  ...             70  423.524902\n",
            "71  2020-04-27  sub_region  ...             71  423.520996\n",
            "72  2020-04-28  sub_region  ...             72  423.510651\n",
            "73  2020-04-29  sub_region  ...             73  423.502411\n",
            "74  2020-04-30  sub_region  ...             74  423.487152\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/media/plotly/test_plot_14_0bb7c432.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: eplaca55 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/media/plotly/test_plot_all_15_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174645-eplaca55/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: eplaca55\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: hsqsy0f0 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: hsqsy0f0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/hsqsy0f0\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/hsqsy0f0</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/hsqsy0f0\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Running runs: ['hsqsy0f0']\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/media/graph/graph_0_summary_51352134.graph.json\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "24.238217145204544\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.9322391209694055\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.11776690930128\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.6199141118962032\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.947837613523006\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.6518399082124233\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Network error resolved after 0:00:13.670767, resuming normal operation.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.64614214003086\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.6786977746165715\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.301386021077633\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.6269763854260628\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.042810605838895\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.6170311771476498\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.744363751262426\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.6055524519716318\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/wandb-metadata.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.016306111589074\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.6160117735226567\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.813427444547415\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.6082087478672082\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.24589281156659\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.6248420312140996\n",
            "Data saved to: \n",
            "28_May_202005_47PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_47PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/config.yaml\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  509.934052\n",
            "66  2020-04-22  sub_region  ...             66  507.839417\n",
            "67  2020-04-23  sub_region  ...             67  509.408234\n",
            "68  2020-04-24  sub_region  ...             68  509.694031\n",
            "69  2020-04-25  sub_region  ...             69  509.575317\n",
            "70  2020-04-26  sub_region  ...             70  508.830750\n",
            "71  2020-04-27  sub_region  ...             71  509.065674\n",
            "72  2020-04-28  sub_region  ...             72  507.174622\n",
            "73  2020-04-29  sub_region  ...             73  509.588074\n",
            "74  2020-04-30  sub_region  ...             74  507.856110\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/wandb-summary.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/media/plotly/test_plot_20_75179c3f.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: hsqsy0f0 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174706-hsqsy0f0/wandb-metadata.json\n",
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            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
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            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
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            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
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            "wandb: Agent Starting Run: kd0jv4c2 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: kd0jv4c2\n"
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            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/kd0jv4c2\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/kd0jv4c2</a><br/>\n",
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            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
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          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174739-kd0jv4c2/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174739-kd0jv4c2/media/graph/graph_0_summary_7dd40716.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174739-kd0jv4c2/requirements.txt\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "24.46081606298685\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.9408006178071866\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174739-kd0jv4c2/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174739-kd0jv4c2/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.266936726868153\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.625651412571852\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174739-kd0jv4c2/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174739-kd0jv4c2/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.894336327910423\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.6497821664580932\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174739-kd0jv4c2/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174739-kd0jv4c2/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.256735399365425\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.6637205922832856\n",
            "The running loss is:\n",
            "16.231802832335234\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.6243001089359705\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174739-kd0jv4c2/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174739-kd0jv4c2/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.1003286074847\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.6192434079801807\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174739-kd0jv4c2/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174739-kd0jv4c2/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.810061607509851\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.6080792925965327\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174739-kd0jv4c2/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174739-kd0jv4c2/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.048187194392085\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.6172379690150802\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174739-kd0jv4c2/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174739-kd0jv4c2/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174739-kd0jv4c2/wandb-metadata.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.809576485306025\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.6080606340502317\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_47PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_47PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174739-kd0jv4c2/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174739-kd0jv4c2/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174739-kd0jv4c2/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  529.040771\n",
            "66  2020-04-22  sub_region  ...             66  528.143555\n",
            "67  2020-04-23  sub_region  ...             67  528.838806\n",
            "68  2020-04-24  sub_region  ...             68  528.961365\n",
            "69  2020-04-25  sub_region  ...             69  528.801025\n",
            "70  2020-04-26  sub_region  ...             70  528.515015\n",
            "71  2020-04-27  sub_region  ...             71  528.581177\n",
            "72  2020-04-28  sub_region  ...             72  528.077698\n",
            "73  2020-04-29  sub_region  ...             73  528.772461\n",
            "74  2020-04-30  sub_region  ...             74  528.409302\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174739-kd0jv4c2/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174739-kd0jv4c2/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174739-kd0jv4c2/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174739-kd0jv4c2/media/plotly/test_plot_18_712b0439.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174739-kd0jv4c2/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174739-kd0jv4c2/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: kd0jv4c2 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174739-kd0jv4c2/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174739-kd0jv4c2/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174739-kd0jv4c2/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174739-kd0jv4c2/media/plotly/test_plot_all_19_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174739-kd0jv4c2/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174739-kd0jv4c2/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: kd0jv4c2\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: b6eh4dgx with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: b6eh4dgx\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/b6eh4dgx\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/b6eh4dgx</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/b6eh4dgx\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmI2ZWg0ZGd4OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174805-b6eh4dgx/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174805-b6eh4dgx/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174805-b6eh4dgx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174805-b6eh4dgx/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174805-b6eh4dgx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174805-b6eh4dgx/media/graph/graph_0_summary_11cf6ea0.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174805-b6eh4dgx/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174805-b6eh4dgx/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174805-b6eh4dgx/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "24.921816915273666\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.958531419818218\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174805-b6eh4dgx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174805-b6eh4dgx/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.33100463449955\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.858884793634598\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174805-b6eh4dgx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174805-b6eh4dgx/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.428308714181185\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.7857041813146609\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174805-b6eh4dgx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174805-b6eh4dgx/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.427796185016632\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.6702998532698705\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['b6eh4dgx']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.2829820625484\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.6647300793287846\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174805-b6eh4dgx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174805-b6eh4dgx/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "2\n",
            "The running loss is:\n",
            "15.887216325849295\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.6110467817634344\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_48PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174805-b6eh4dgx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174805-b6eh4dgx/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_48PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  559.779724\n",
            "66  2020-04-22  sub_region  ...             66  560.009094\n",
            "67  2020-04-23  sub_region  ...             67  560.145813\n",
            "68  2020-04-24  sub_region  ...             68  560.165466\n",
            "69  2020-04-25  sub_region  ...             69  560.001709\n",
            "70  2020-04-26  sub_region  ...             70  560.061584\n",
            "71  2020-04-27  sub_region  ...             71  559.992859\n",
            "72  2020-04-28  sub_region  ...             72  560.228088\n",
            "73  2020-04-29  sub_region  ...             73  559.984314\n",
            "74  2020-04-30  sub_region  ...             74  560.468567\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174805-b6eh4dgx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174805-b6eh4dgx/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174805-b6eh4dgx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174805-b6eh4dgx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174805-b6eh4dgx/media/plotly/test_plot_12_f72c3f2f.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174805-b6eh4dgx/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174805-b6eh4dgx/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: b6eh4dgx \n",
            "\n"
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          "name": "stdout"
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          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174805-b6eh4dgx/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174805-b6eh4dgx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174805-b6eh4dgx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174805-b6eh4dgx/media/plotly/test_plot_all_13_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174805-b6eh4dgx/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174805-b6eh4dgx/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: b6eh4dgx\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: m74n1snd with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: m74n1snd\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/m74n1snd\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/m74n1snd</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/m74n1snd\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOm03NG4xc25kOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174822-m74n1snd/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174822-m74n1snd/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174822-m74n1snd/media/graph/graph_0_summary_45d53722.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174822-m74n1snd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174822-m74n1snd/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174822-m74n1snd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174822-m74n1snd/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174822-m74n1snd/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174822-m74n1snd/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "25.268363758921623\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.9718601445739086\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174822-m74n1snd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174822-m74n1snd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.410943418741226\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.8619593622592779\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174822-m74n1snd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174822-m74n1snd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.349964793771505\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.7826909536065964\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['m74n1snd']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174822-m74n1snd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174822-m74n1snd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.21460761502385\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.6621002928855327\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174822-m74n1snd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174822-m74n1snd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.72370159626007\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.6816808306253873\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174822-m74n1snd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174822-m74n1snd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.540141738951206\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.597697759190431\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_48PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_48PM_model.pth\n",
            "interpolate should be below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174822-m74n1snd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174822-m74n1snd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174822-m74n1snd/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  559.067444\n",
            "66  2020-04-22  sub_region  ...             66  559.143799\n",
            "67  2020-04-23  sub_region  ...             67  559.442505\n",
            "68  2020-04-24  sub_region  ...             68  559.442566\n",
            "69  2020-04-25  sub_region  ...             69  559.225464\n",
            "70  2020-04-26  sub_region  ...             70  559.264404\n",
            "71  2020-04-27  sub_region  ...             71  559.198975\n",
            "72  2020-04-28  sub_region  ...             72  559.425171\n",
            "73  2020-04-29  sub_region  ...             73  559.358643\n",
            "74  2020-04-30  sub_region  ...             74  559.806274\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174822-m74n1snd/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174822-m74n1snd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174822-m74n1snd/wandb-history.jsonl\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174822-m74n1snd/media/plotly/test_plot_12_b8d9f787.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174822-m74n1snd/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174822-m74n1snd/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: m74n1snd \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174822-m74n1snd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174822-m74n1snd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174822-m74n1snd/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174822-m74n1snd/media/plotly/test_plot_all_13_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174822-m74n1snd/media/plotly\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174822-m74n1snd/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: m74n1snd\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: s7ektckx with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: s7ektckx\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/s7ektckx\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/s7ektckx</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/s7ektckx\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnM3ZWt0Y2t4OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/media/graph/graph_0_summary_74300212.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/requirements.txt\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.4773468375206\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.819093873500824\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.37137946486473\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.7748551785945892\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.317528434097767\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.6527011373639107\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.260967433452606\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.5704386973381043\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['s7ektckx']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.17140232771635\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.566856093108654\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.054600022733212\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.5621840009093284\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.07607826590538\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.5630431306362152\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.025826334953308\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.5610330533981324\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.500305883586407\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.5400122353434562\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.7346723228693\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.509386892914772\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_48PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_48PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  384.512299\n",
            "66  2020-04-22  sub_region  ...             66  383.916504\n",
            "67  2020-04-23  sub_region  ...             67  384.334045\n",
            "68  2020-04-24  sub_region  ...             68  384.549500\n",
            "69  2020-04-25  sub_region  ...             69  384.431915\n",
            "70  2020-04-26  sub_region  ...             70  384.146332\n",
            "71  2020-04-27  sub_region  ...             71  384.198578\n",
            "72  2020-04-28  sub_region  ...             72  383.738586\n",
            "73  2020-04-29  sub_region  ...             73  384.381348\n",
            "74  2020-04-30  sub_region  ...             74  383.990662\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/media/plotly/test_plot_20_1987c841.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: s7ektckx \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174838-s7ektckx/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: s7ektckx\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: cda37iga with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: cda37iga\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/cda37iga\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/cda37iga</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/cda37iga\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmNkYTM3aWdhOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/media/graph/graph_0_summary_a747039c.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.519908547401428\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.8207963418960571\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.348501980304718\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.7739400792121888\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.020245015621185\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.6408098006248474\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.293965496122837\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.5717586198449135\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['cda37iga']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.129456043243408\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.5651782417297363\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "2\n",
            "The running loss is:\n",
            "13.794427633285522\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.5517771053314209\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.02604092657566\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.5210416370630264\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.591085441410542\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.3836434176564217\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.496750168502331\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.29987000674009323\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_49PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_49PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  388.992981\n",
            "66  2020-04-22  sub_region  ...             66  387.531372\n",
            "67  2020-04-23  sub_region  ...             67  388.493744\n",
            "68  2020-04-24  sub_region  ...             68  388.772583\n",
            "69  2020-04-25  sub_region  ...             69  388.547852\n",
            "70  2020-04-26  sub_region  ...             70  387.934692\n",
            "71  2020-04-27  sub_region  ...             71  388.074402\n",
            "72  2020-04-28  sub_region  ...             72  387.295563\n",
            "73  2020-04-29  sub_region  ...             73  388.444427\n",
            "74  2020-04-30  sub_region  ...             74  387.736328\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/wandb-history.jsonl\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/media/plotly/test_plot_18_166901d4.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: cda37iga \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174900-cda37iga/wandb-history.jsonl\n",
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            "INFO:wandb.wandb_agent:Cleaning up finished run: cda37iga\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
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            "\tlr: 0.002\n",
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            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
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            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: xoduj0zf\n"
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          "name": "stdout"
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          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/xoduj0zf\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/xoduj0zf</a><br/>\n",
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          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "11.178941723890603\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.4140348786626149\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174916-xoduj0zf/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174916-xoduj0zf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174916-xoduj0zf/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174916-xoduj0zf/wandb-history.jsonl\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174916-xoduj0zf/requirements.txt\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.482956442050636\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.5364057941500235\n",
            "1\n",
            "The running loss is:\n",
            "12.16446726070717\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.4505358244706359\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174916-xoduj0zf/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174916-xoduj0zf/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.8048704912635\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.2890692774542037\n",
            "1\n",
            "The running loss is:\n",
            "7.099390174262226\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.26294037682452687\n",
            "The running loss is:\n",
            "6.51896614592988\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.24144319058999558\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174916-xoduj0zf/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174916-xoduj0zf/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.50308578943077\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.24085502923817667\n",
            "1\n",
            "The running loss is:\n",
            "6.709017640678212\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.24848213483993378\n",
            "The running loss is:\n",
            "6.639082251727814\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.2458919352491783\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174916-xoduj0zf/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174916-xoduj0zf/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.743373372781207\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.21271753232522989\n",
            "Data saved to: \n",
            "28_May_202005_49PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_49PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['xoduj0zf']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174916-xoduj0zf/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174916-xoduj0zf/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174916-xoduj0zf/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  758.606506\n",
            "66  2020-04-22  sub_region  ...             66  838.690002\n",
            "67  2020-04-23  sub_region  ...             67  739.158081\n",
            "68  2020-04-24  sub_region  ...             68  760.182251\n",
            "69  2020-04-25  sub_region  ...             69  791.689392\n",
            "70  2020-04-26  sub_region  ...             70  821.668945\n",
            "71  2020-04-27  sub_region  ...             71  813.191467\n",
            "72  2020-04-28  sub_region  ...             72  824.506714\n",
            "73  2020-04-29  sub_region  ...             73  810.240173\n",
            "74  2020-04-30  sub_region  ...             74  760.039307\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174916-xoduj0zf/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174916-xoduj0zf/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174916-xoduj0zf/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174916-xoduj0zf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174916-xoduj0zf/media/plotly/test_plot_20_50b5ba08.plotly.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174916-xoduj0zf/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174916-xoduj0zf/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: xoduj0zf \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174916-xoduj0zf/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174916-xoduj0zf/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174916-xoduj0zf/wandb-summary.json\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174916-xoduj0zf/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174916-xoduj0zf/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174916-xoduj0zf/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: xoduj0zf\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: br2d7992 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: br2d7992\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/br2d7992\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/br2d7992</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/br2d7992\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmJyMmQ3OTkyOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "11.243638909421861\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.41643107071932817\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174929-br2d7992/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174929-br2d7992/media/graph/graph_0_summary_cf4d9cf1.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174929-br2d7992/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174929-br2d7992/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174929-br2d7992/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174929-br2d7992/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174929-br2d7992/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174929-br2d7992/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174929-br2d7992/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.065432488219813\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.5579789810451783\n",
            "1\n",
            "The running loss is:\n",
            "9.927668149583042\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.36769141294752006\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174929-br2d7992/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174929-br2d7992/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.617937269620597\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.2821458248007629\n",
            "1\n",
            "The running loss is:\n",
            "6.723564739339054\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.2490209162718168\n",
            "The running loss is:\n",
            "7.1353028768789954\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.2642704769214443\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174929-br2d7992/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174929-br2d7992/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.726659771054983\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.21209851003907346\n",
            "2\n",
            "The running loss is:\n",
            "6.842962785856798\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.2534430661428444\n",
            "The running loss is:\n",
            "6.147264284198172\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.22767645497030267\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174929-br2d7992/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174929-br2d7992/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.772955868189456\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.25085021734035023\n",
            "Data saved to: \n",
            "28_May_202005_49PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_49PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['br2d7992']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174929-br2d7992/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174929-br2d7992/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174929-br2d7992/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  702.101074\n",
            "66  2020-04-22  sub_region  ...             66  799.573730\n",
            "67  2020-04-23  sub_region  ...             67  688.857788\n",
            "68  2020-04-24  sub_region  ...             68  695.529663\n",
            "69  2020-04-25  sub_region  ...             69  724.923462\n",
            "70  2020-04-26  sub_region  ...             70  761.772095\n",
            "71  2020-04-27  sub_region  ...             71  748.695679\n",
            "72  2020-04-28  sub_region  ...             72  803.100220\n",
            "73  2020-04-29  sub_region  ...             73  733.273804\n",
            "74  2020-04-30  sub_region  ...             74  718.350464\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174929-br2d7992/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174929-br2d7992/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174929-br2d7992/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174929-br2d7992/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174929-br2d7992/media/plotly/test_plot_20_e049cd21.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174929-br2d7992/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174929-br2d7992/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
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          "output_type": "stream",
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            "wandb: Agent Finished Run: br2d7992 \n",
            "\n"
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174929-br2d7992/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174929-br2d7992/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174929-br2d7992/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174929-br2d7992/media/plotly\n",
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            "INFO:wandb.wandb_agent:Cleaning up finished run: br2d7992\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
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            "wandb: Agent Starting Run: ftqp9o2w with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: ftqp9o2w\n"
          ],
          "name": "stdout"
        },
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          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/ftqp9o2w\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/ftqp9o2w</a><br/>\n",
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        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "13.754779417067766\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.5094362747062136\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174945-ftqp9o2w/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174945-ftqp9o2w/wandb-history.jsonl\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174945-ftqp9o2w/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174945-ftqp9o2w/wandb-metadata.json\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.335549898445606\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.5679833295720594\n",
            "1\n",
            "The running loss is:\n",
            "12.792411483824253\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.4737930179194168\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174945-ftqp9o2w/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174945-ftqp9o2w/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.463905839249492\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.38755206812035153\n",
            "1\n",
            "The running loss is:\n",
            "8.178016487509012\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.3028894995373708\n",
            "The running loss is:\n",
            "7.196833549998701\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.2665493907406926\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174945-ftqp9o2w/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174945-ftqp9o2w/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.724783644080162\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.2861030979288949\n",
            "2\n",
            "The running loss is:\n",
            "7.328360717743635\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.27142076732383835\n",
            "The running loss is:\n",
            "8.112628399394453\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.30046771849609083\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174945-ftqp9o2w/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174945-ftqp9o2w/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "6.612887730821967\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.244921767808221\n",
            "Data saved to: \n",
            "28_May_202005_49PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.wandb_agent:Running runs: ['ftqp9o2w']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_49PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174945-ftqp9o2w/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174945-ftqp9o2w/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174945-ftqp9o2w/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  785.347290\n",
            "66  2020-04-22  sub_region  ...             66  848.155396\n",
            "67  2020-04-23  sub_region  ...             67  778.021973\n",
            "68  2020-04-24  sub_region  ...             68  802.694946\n",
            "69  2020-04-25  sub_region  ...             69  814.782837\n",
            "70  2020-04-26  sub_region  ...             70  827.907227\n",
            "71  2020-04-27  sub_region  ...             71  817.994751\n",
            "72  2020-04-28  sub_region  ...             72  834.097107\n",
            "73  2020-04-29  sub_region  ...             73  812.438049\n",
            "74  2020-04-30  sub_region  ...             74  797.486328\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174945-ftqp9o2w/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174945-ftqp9o2w/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174945-ftqp9o2w/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174945-ftqp9o2w/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174945-ftqp9o2w/media/plotly/test_plot_20_e9ff783e.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174945-ftqp9o2w/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174945-ftqp9o2w/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: ftqp9o2w \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174945-ftqp9o2w/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174945-ftqp9o2w/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174945-ftqp9o2w/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174945-ftqp9o2w/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174945-ftqp9o2w/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_174945-ftqp9o2w/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: ftqp9o2w\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: rugrz0ma with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: rugrz0ma\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/rugrz0ma\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/rugrz0ma</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/rugrz0ma\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnJ1Z3J6MG1hOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "13.334896691143513\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.4938850626349449\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175001-rugrz0ma/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175001-rugrz0ma/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175001-rugrz0ma/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175001-rugrz0ma/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175001-rugrz0ma/media/graph/graph_0_summary_54797028.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175001-rugrz0ma/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175001-rugrz0ma/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175001-rugrz0ma/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175001-rugrz0ma/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.006994970142841\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.5558146285238089\n",
            "1\n",
            "The running loss is:\n",
            "11.964153863489628\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.44311680975887513\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175001-rugrz0ma/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175001-rugrz0ma/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.46366179548204\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.3505059924252607\n",
            "1\n",
            "The running loss is:\n",
            "7.935978587716818\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.29392513287840066\n",
            "The running loss is:\n",
            "7.160205790773034\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.26519280706566795\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175001-rugrz0ma/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175001-rugrz0ma/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.432532018050551\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.2752789636315019\n",
            "2\n",
            "The running loss is:\n",
            "6.930170862004161\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.256672994889043\n",
            "The running loss is:\n",
            "7.583688434679061\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.2808773494325578\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175001-rugrz0ma/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175001-rugrz0ma/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.852206656709313\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.2537854317299746\n",
            "Data saved to: \n",
            "28_May_202005_50PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_50PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['rugrz0ma']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175001-rugrz0ma/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  792.838745\n",
            "66  2020-04-22  sub_region  ...             66  839.469238\n",
            "67  2020-04-23  sub_region  ...             67  793.361755\n",
            "68  2020-04-24  sub_region  ...             68  806.429504\n",
            "69  2020-04-25  sub_region  ...             69  816.609619\n",
            "70  2020-04-26  sub_region  ...             70  828.707764\n",
            "71  2020-04-27  sub_region  ...             71  823.823608\n",
            "72  2020-04-28  sub_region  ...             72  829.279419\n",
            "73  2020-04-29  sub_region  ...             73  820.153503\n",
            "74  2020-04-30  sub_region  ...             74  806.989746\n",
            "\n",
            "[21 rows x 28 columns]"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175001-rugrz0ma/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175001-rugrz0ma/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175001-rugrz0ma/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175001-rugrz0ma/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175001-rugrz0ma/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175001-rugrz0ma/media/plotly/test_plot_20_4bc95361.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175001-rugrz0ma/media\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175001-rugrz0ma/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: rugrz0ma \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175001-rugrz0ma/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175001-rugrz0ma/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175001-rugrz0ma/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175001-rugrz0ma/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175001-rugrz0ma/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: rugrz0ma\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: jheap938 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: jheap938\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/jheap938\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/jheap938</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/jheap938\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175018-jheap938/wandb-metadata.json\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmpoZWFwOTM4OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175018-jheap938/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175018-jheap938/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175018-jheap938/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175018-jheap938/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175018-jheap938/wandb-events.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.989742126315832\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.7303746971659936\n",
            "The running loss is:\n",
            "16.38419210910797\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.6301612349656912\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175018-jheap938/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175018-jheap938/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175018-jheap938/media/graph/graph_0_summary_6bbc3677.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175018-jheap938/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175018-jheap938/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.279926925897598\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.5876894971499076\n",
            "The running loss is:\n",
            "10.945733822882175\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.4209897624185452\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['jheap938']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.606289468705654\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.21562651802714056\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175018-jheap938/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175018-jheap938/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.256948810070753\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.16372880038733667\n",
            "The running loss is:\n",
            "4.631639575585723\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.17813998367637396\n",
            "1\n",
            "The running loss is:\n",
            "6.9152334509417415\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.26597051734391314\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175018-jheap938/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175018-jheap938/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "3.967709585092962\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.15260421481126776\n",
            "The running loss is:\n",
            "3.5715523255057633\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.13736739713483706\n",
            "Data saved to: \n",
            "28_May_202005_50PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175018-jheap938/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175018-jheap938/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_50PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  814.696899\n",
            "66  2020-04-22  sub_region  ...             66  825.249207\n",
            "67  2020-04-23  sub_region  ...             67  843.944824\n",
            "68  2020-04-24  sub_region  ...             68  816.804077\n",
            "69  2020-04-25  sub_region  ...             69  796.358887\n",
            "70  2020-04-26  sub_region  ...             70  799.779358\n",
            "71  2020-04-27  sub_region  ...             71  801.697021\n",
            "72  2020-04-28  sub_region  ...             72  800.930298\n",
            "73  2020-04-29  sub_region  ...             73  815.445740\n",
            "74  2020-04-30  sub_region  ...             74  822.305115\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175018-jheap938/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175018-jheap938/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: jheap938 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175018-jheap938/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175018-jheap938/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175018-jheap938/media/plotly/test_plot_20_d82f34c9.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175018-jheap938/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175018-jheap938/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175018-jheap938/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175018-jheap938/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175018-jheap938/wandb-events.jsonl\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: jheap938\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 3tx202d6 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 3tx202d6\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/3tx202d6\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/3tx202d6</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/3tx202d6\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjN0eDIwMmQ2OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "18.905425552278757\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.7271317520107214\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175034-3tx202d6/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175034-3tx202d6/media/graph/graph_0_summary_759919e1.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175034-3tx202d6/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175034-3tx202d6/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175034-3tx202d6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175034-3tx202d6/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175034-3tx202d6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175034-3tx202d6/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175034-3tx202d6/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.324342384934425\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.6278593224974779\n",
            "1\n",
            "The running loss is:\n",
            "15.281524695456028\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.5877509498252318\n",
            "The running loss is:\n",
            "10.686566263437271\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.41102177936297196\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175034-3tx202d6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175034-3tx202d6/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "6.059355471283197\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.2330521335108922\n",
            "The running loss is:\n",
            "4.697019984945655\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.1806546148056021\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175034-3tx202d6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175034-3tx202d6/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.977002476342022\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.26834624909007776\n",
            "1\n",
            "The running loss is:\n",
            "5.771942357998341\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.2219977829999362\n",
            "The running loss is:\n",
            "4.5645317947492\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.17555891518266156\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175034-3tx202d6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175034-3tx202d6/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.686972144991159\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.21872969788427538\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_50PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_50PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['3tx202d6']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175034-3tx202d6/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175034-3tx202d6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175034-3tx202d6/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  736.851746\n",
            "66  2020-04-22  sub_region  ...             66  772.893066\n",
            "67  2020-04-23  sub_region  ...             67  762.027588\n",
            "68  2020-04-24  sub_region  ...             68  752.332520\n",
            "69  2020-04-25  sub_region  ...             69  736.720947\n",
            "70  2020-04-26  sub_region  ...             70  741.464417\n",
            "71  2020-04-27  sub_region  ...             71  733.462769\n",
            "72  2020-04-28  sub_region  ...             72  760.639526\n",
            "73  2020-04-29  sub_region  ...             73  746.799805\n",
            "74  2020-04-30  sub_region  ...             74  770.807861\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175034-3tx202d6/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175034-3tx202d6/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175034-3tx202d6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175034-3tx202d6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175034-3tx202d6/media/plotly/test_plot_20_556d85cf.plotly.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175034-3tx202d6/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175034-3tx202d6/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 3tx202d6 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175034-3tx202d6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175034-3tx202d6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175034-3tx202d6/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175034-3tx202d6/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175034-3tx202d6/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175034-3tx202d6/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 3tx202d6\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: qoi6pait with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: qoi6pait\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/qoi6pait\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/qoi6pait</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/qoi6pait\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnFvaTZwYWl0OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "16.948153391480446\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.6518520535184786\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175051-qoi6pait/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175051-qoi6pait/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175051-qoi6pait/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175051-qoi6pait/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175051-qoi6pait/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175051-qoi6pait/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175051-qoi6pait/media/graph/graph_0_summary_0475200c.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175051-qoi6pait/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175051-qoi6pait/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.85379159078002\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.5328381381069238\n",
            "The running loss is:\n",
            "9.64835274219513\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.37109049008442807\n",
            "1\n",
            "The running loss is:\n",
            "8.4732926171273\n",
            "The number of items in train is: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175051-qoi6pait/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175051-qoi6pait/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "26\n",
            "The loss for epoch 3\n",
            "0.3258958698895115\n",
            "The running loss is:\n",
            "5.7244476936757565\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.22017106514137524\n",
            "The running loss is:\n",
            "6.5513798370957375\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.25197614758060527\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175051-qoi6pait/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175051-qoi6pait/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.37942417897284\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.3222855453451092\n",
            "The running loss is:\n",
            "4.463485406711698\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.1716725156427576\n",
            "1\n",
            "The running loss is:\n",
            "4.722438246943057\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.18163224026704064\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175051-qoi6pait/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175051-qoi6pait/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.340588949620724\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.1669457288315663\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_50PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_50PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['qoi6pait']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175051-qoi6pait/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175051-qoi6pait/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175051-qoi6pait/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  851.006592\n",
            "66  2020-04-22  sub_region  ...             66  861.678711\n",
            "67  2020-04-23  sub_region  ...             67  870.574524\n",
            "68  2020-04-24  sub_region  ...             68  854.424133\n",
            "69  2020-04-25  sub_region  ...             69  835.877502\n",
            "70  2020-04-26  sub_region  ...             70  840.583374\n",
            "71  2020-04-27  sub_region  ...             71  839.167969\n",
            "72  2020-04-28  sub_region  ...             72  850.335999\n",
            "73  2020-04-29  sub_region  ...             73  848.938354\n",
            "74  2020-04-30  sub_region  ...             74  859.935547\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175051-qoi6pait/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175051-qoi6pait/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175051-qoi6pait/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175051-qoi6pait/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175051-qoi6pait/media/plotly/test_plot_20_0f4d1d99.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175051-qoi6pait/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175051-qoi6pait/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: qoi6pait \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175051-qoi6pait/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175051-qoi6pait/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175051-qoi6pait/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175051-qoi6pait/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175051-qoi6pait/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175051-qoi6pait/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: qoi6pait\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: f2z9xtis with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: f2z9xtis\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/f2z9xtis\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/f2z9xtis</a><br/>\n",
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/f2z9xtis\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmYyejl4dGlzOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "16.634709760546684\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.6397965292517955\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175108-f2z9xtis/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175108-f2z9xtis/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175108-f2z9xtis/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175108-f2z9xtis/media/graph/graph_0_summary_52394716.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175108-f2z9xtis/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175108-f2z9xtis/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175108-f2z9xtis/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175108-f2z9xtis/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175108-f2z9xtis/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.594469293951988\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.5613257420750765\n",
            "The running loss is:\n",
            "9.017393708229065\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.3468228349318871\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175108-f2z9xtis/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175108-f2z9xtis/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "7.2338755913078785\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.27822598428107226\n",
            "The running loss is:\n",
            "4.978128891438246\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.19146649582454792\n",
            "1\n",
            "The running loss is:\n",
            "5.498803533613682\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.21149244360052621\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175108-f2z9xtis/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175108-f2z9xtis/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.14033218100667\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.27462816080794883\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_51PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_51PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  736.526611\n",
            "66  2020-04-22  sub_region  ...             66  758.064575\n",
            "67  2020-04-23  sub_region  ...             67  751.773193\n",
            "68  2020-04-24  sub_region  ...             68  746.766174\n",
            "69  2020-04-25  sub_region  ...             69  738.920166\n",
            "70  2020-04-26  sub_region  ...             70  743.997314\n",
            "71  2020-04-27  sub_region  ...             71  741.321045\n",
            "72  2020-04-28  sub_region  ...             72  750.091064\n",
            "73  2020-04-29  sub_region  ...             73  743.901367\n",
            "74  2020-04-30  sub_region  ...             74  754.618958\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175108-f2z9xtis/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175108-f2z9xtis/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175108-f2z9xtis/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "INFO:wandb.wandb_agent:Running runs: ['f2z9xtis']\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: f2z9xtis \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175108-f2z9xtis/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175108-f2z9xtis/media/plotly/test_plot_14_251cab71.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175108-f2z9xtis/media/plotly/test_plot_all_15_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175108-f2z9xtis/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175108-f2z9xtis/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175108-f2z9xtis/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175108-f2z9xtis/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: f2z9xtis\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 56hcdtcy with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 56hcdtcy\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/56hcdtcy\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/56hcdtcy</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/56hcdtcy\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjU2aGNkdGN5OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "14.528097435832024\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.581123897433281\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175120-56hcdtcy/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175120-56hcdtcy/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175120-56hcdtcy/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175120-56hcdtcy/wandb-events.jsonl\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175120-56hcdtcy/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175120-56hcdtcy/wandb-history.jsonl\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.321749061346054\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.5728699624538421\n",
            "The running loss is:\n",
            "8.53690830618143\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.3414763322472572\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175120-56hcdtcy/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175120-56hcdtcy/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "3.877267986536026\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.15509071946144104\n",
            "The running loss is:\n",
            "3.9804319692775607\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.15921727877110242\n",
            "1\n",
            "The running loss is:\n",
            "4.088011069223285\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.16352044276893138\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175120-56hcdtcy/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175120-56hcdtcy/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "3.642200954724103\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.14568803818896414\n",
            "1\n",
            "The running loss is:\n",
            "4.692892727442086\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.18771570909768343\n",
            "2\n",
            "The running loss is:\n",
            "3.2474756240844727\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.1298990249633789\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175120-56hcdtcy/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175120-56hcdtcy/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "3.19531544810161\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.1278126179240644\n",
            "Data saved to: \n",
            "28_May_202005_51PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_51PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['56hcdtcy']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175120-56hcdtcy/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175120-56hcdtcy/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175120-56hcdtcy/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  711.413086\n",
            "66  2020-04-22  sub_region  ...             66  714.489502\n",
            "67  2020-04-23  sub_region  ...             67  749.420166\n",
            "68  2020-04-24  sub_region  ...             68  730.427063\n",
            "69  2020-04-25  sub_region  ...             69  707.012146\n",
            "70  2020-04-26  sub_region  ...             70  700.846252\n",
            "71  2020-04-27  sub_region  ...             71  701.634644\n",
            "72  2020-04-28  sub_region  ...             72  702.439697\n",
            "73  2020-04-29  sub_region  ...             73  710.740356\n",
            "74  2020-04-30  sub_region  ...             74  736.382812\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175120-56hcdtcy/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175120-56hcdtcy/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175120-56hcdtcy/wandb-summary.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175120-56hcdtcy/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175120-56hcdtcy/media/plotly/test_plot_20_b4b65440.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175120-56hcdtcy/media\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175120-56hcdtcy/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 56hcdtcy \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175120-56hcdtcy/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175120-56hcdtcy/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175120-56hcdtcy/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175120-56hcdtcy/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175120-56hcdtcy/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175120-56hcdtcy/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 56hcdtcy\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: aiezfxcc with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: aiezfxcc\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/aiezfxcc\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/aiezfxcc</a><br/>\n",
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          "metadata": {
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        {
          "output_type": "stream",
          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/aiezfxcc\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmFpZXpmeGNjOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "14.583468616008759\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.5833387446403503\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175138-aiezfxcc/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175138-aiezfxcc/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175138-aiezfxcc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175138-aiezfxcc/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175138-aiezfxcc/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175138-aiezfxcc/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175138-aiezfxcc/media/graph/graph_0_summary_aa20f3ff.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175138-aiezfxcc/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175138-aiezfxcc/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.234198205173016\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.4493679282069206\n",
            "1\n",
            "The running loss is:\n",
            "6.8342317044734955\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.2733692681789398\n",
            "2\n",
            "The running loss is:\n",
            "4.914771635085344\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.19659086540341378\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175138-aiezfxcc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175138-aiezfxcc/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.097113943658769\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.24388455774635076\n",
            "1\n",
            "The running loss is:\n",
            "5.855831284075975\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.23423325136303902\n",
            "2\n",
            "The running loss is:\n",
            "4.823610462248325\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.19294441848993302\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175138-aiezfxcc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175138-aiezfxcc/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.116255223751068\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.2046502089500427\n",
            "1\n",
            "The running loss is:\n",
            "3.9354080138728023\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.1574163205549121\n",
            "2\n",
            "The running loss is:\n",
            "3.635712749324739\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.14542850997298956\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175138-aiezfxcc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175138-aiezfxcc/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_51PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_51PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['aiezfxcc']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  742.793396\n",
            "66  2020-04-22  sub_region  ...             66  736.652344\n",
            "67  2020-04-23  sub_region  ...             67  754.830688\n",
            "68  2020-04-24  sub_region  ...             68  749.173950\n",
            "69  2020-04-25  sub_region  ...             69  738.829346\n",
            "70  2020-04-26  sub_region  ...             70  734.617004\n",
            "71  2020-04-27  sub_region  ...             71  732.794556\n",
            "72  2020-04-28  sub_region  ...             72  736.974976\n",
            "73  2020-04-29  sub_region  ...             73  737.531616\n",
            "74  2020-04-30  sub_region  ...             74  753.737549\n",
            "\n",
            "[21 rows x 28 columns]"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175138-aiezfxcc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175138-aiezfxcc/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
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          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175138-aiezfxcc/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175138-aiezfxcc/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175138-aiezfxcc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175138-aiezfxcc/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175138-aiezfxcc/media/plotly/test_plot_20_71421788.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175138-aiezfxcc/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175138-aiezfxcc/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: aiezfxcc \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175138-aiezfxcc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175138-aiezfxcc/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175138-aiezfxcc/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175138-aiezfxcc/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175138-aiezfxcc/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175138-aiezfxcc/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: aiezfxcc\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 01v7tee2 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 01v7tee2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/01v7tee2\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/01v7tee2</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/01v7tee2\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjAxdjd0ZWUyOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175154-01v7tee2/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175154-01v7tee2/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175154-01v7tee2/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175154-01v7tee2/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175154-01v7tee2/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175154-01v7tee2/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175154-01v7tee2/media/graph/graph_0_summary_ee2261c4.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175154-01v7tee2/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175154-01v7tee2/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.349453534625354\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.4573871679490872\n",
            "The running loss is:\n",
            "30.535183822968975\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.130932734184036\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175154-01v7tee2/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175154-01v7tee2/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.304419979453087\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.6779414807204847\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175154-01v7tee2/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175154-01v7tee2/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.870923589682207\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.4026267996178595\n",
            "2\n",
            "The running loss is:\n",
            "9.898105231113732\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.36659649004124933\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_51PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175154-01v7tee2/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175154-01v7tee2/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_51PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['01v7tee2']\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  502.529541\n",
            "66  2020-04-22  sub_region  ...             66  492.594849\n",
            "67  2020-04-23  sub_region  ...             67  496.524628\n",
            "68  2020-04-24  sub_region  ...             68  495.569824\n",
            "69  2020-04-25  sub_region  ...             69  495.434570\n",
            "70  2020-04-26  sub_region  ...             70  493.173859\n",
            "71  2020-04-27  sub_region  ...             71  493.890808\n",
            "72  2020-04-28  sub_region  ...             72  491.183136\n",
            "73  2020-04-29  sub_region  ...             73  497.434570\n",
            "74  2020-04-30  sub_region  ...             74  489.901245\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175154-01v7tee2/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175154-01v7tee2/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175154-01v7tee2/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175154-01v7tee2/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175154-01v7tee2/media/plotly/test_plot_all_11_6655637c.plotly.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 01v7tee2 \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175154-01v7tee2/media/plotly/test_plot_10_c557f1bd.plotly.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175154-01v7tee2/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175154-01v7tee2/media/plotly\n",
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175154-01v7tee2/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175154-01v7tee2/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175154-01v7tee2/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175154-01v7tee2/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175154-01v7tee2/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 01v7tee2\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: dziok6s7 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: dziok6s7\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/dziok6s7\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/dziok6s7</a><br/>\n",
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          "metadata": {
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        {
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/dziok6s7\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmR6aW9rNnM3OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175205-dziok6s7/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175205-dziok6s7/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175205-dziok6s7/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175205-dziok6s7/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175205-dziok6s7/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175205-dziok6s7/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175205-dziok6s7/media/graph/graph_0_summary_0ec97a73.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175205-dziok6s7/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175205-dziok6s7/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.00125639885664\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.4444909777354311\n",
            "The running loss is:\n",
            "27.564760353183374\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.0209170501179028\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175205-dziok6s7/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175205-dziok6s7/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.1493139838567\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7092338512539519\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175205-dziok6s7/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175205-dziok6s7/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.482985021779314\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.3512216674733079\n",
            "The running loss is:\n",
            "8.379846808500588\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.31036469661113286\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175205-dziok6s7/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175205-dziok6s7/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.564052293615532\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.24311304791168636\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['dziok6s7']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.043116731874761\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.2608561752546208\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175205-dziok6s7/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175205-dziok6s7/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.5104550529504195\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.2411279649240896\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175205-dziok6s7/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175205-dziok6s7/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.045392430853099\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.2609404604019666\n",
            "The running loss is:\n",
            "6.926817309111357\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.25654878922634655\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_52PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175205-dziok6s7/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175205-dziok6s7/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_52PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  778.043945\n",
            "66  2020-04-22  sub_region  ...             66  783.455933\n",
            "67  2020-04-23  sub_region  ...             67  766.487671\n",
            "68  2020-04-24  sub_region  ...             68  773.625916\n",
            "69  2020-04-25  sub_region  ...             69  782.676147\n",
            "70  2020-04-26  sub_region  ...             70  784.909546\n",
            "71  2020-04-27  sub_region  ...             71  784.748901\n",
            "72  2020-04-28  sub_region  ...             72  778.614258\n",
            "73  2020-04-29  sub_region  ...             73  782.117676\n",
            "74  2020-04-30  sub_region  ...             74  761.960938\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175205-dziok6s7/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175205-dziok6s7/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: dziok6s7 \n",
            "\n"
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          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175205-dziok6s7/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175205-dziok6s7/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175205-dziok6s7/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175205-dziok6s7/media/plotly/test_plot_20_647b6c30.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175205-dziok6s7/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175205-dziok6s7/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175205-dziok6s7/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: dziok6s7\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: fgsk32jf with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: fgsk32jf\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/fgsk32jf\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/fgsk32jf</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/fgsk32jf\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmZnc2szMmpmOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175222-fgsk32jf/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175222-fgsk32jf/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175222-fgsk32jf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175222-fgsk32jf/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175222-fgsk32jf/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175222-fgsk32jf/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175222-fgsk32jf/media/graph/graph_0_summary_5f629476.graph.json\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175222-fgsk32jf/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.362042212858796\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.5689645264021777\n",
            "The running loss is:\n",
            "18.604257106781006\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.6890465595104076\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175222-fgsk32jf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175222-fgsk32jf/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.387704892084002\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7180631441512594\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175222-fgsk32jf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175222-fgsk32jf/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.115502156317234\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.6339074872710087\n",
            "The running loss is:\n",
            "9.399747041985393\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.3481387793327923\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175222-fgsk32jf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175222-fgsk32jf/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.062234118580818\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.2986012636511414\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['fgsk32jf']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.508644045330584\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.2780979276048364\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175222-fgsk32jf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175222-fgsk32jf/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.624856340698898\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.2824020866925518\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175222-fgsk32jf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175222-fgsk32jf/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.4580579325556755\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.27622436787243243\n",
            "The running loss is:\n",
            "6.958448266610503\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.25772030617075936\n",
            "Data saved to: \n",
            "28_May_202005_52PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175222-fgsk32jf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175222-fgsk32jf/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_52PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  772.555298\n",
            "66  2020-04-22  sub_region  ...             66  766.406433\n",
            "67  2020-04-23  sub_region  ...             67  773.479492\n",
            "68  2020-04-24  sub_region  ...             68  770.044922\n",
            "69  2020-04-25  sub_region  ...             69  772.980347\n",
            "70  2020-04-26  sub_region  ...             70  772.595215\n",
            "71  2020-04-27  sub_region  ...             71  772.935425\n",
            "72  2020-04-28  sub_region  ...             72  770.757202\n",
            "73  2020-04-29  sub_region  ...             73  772.154602\n",
            "74  2020-04-30  sub_region  ...             74  771.334900\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175222-fgsk32jf/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175222-fgsk32jf/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175222-fgsk32jf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175222-fgsk32jf/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175222-fgsk32jf/media/plotly/test_plot_20_cf3ed598.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175222-fgsk32jf/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175222-fgsk32jf/media/plotly\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: fgsk32jf \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175222-fgsk32jf/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175222-fgsk32jf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175222-fgsk32jf/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175222-fgsk32jf/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175222-fgsk32jf/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175222-fgsk32jf/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: fgsk32jf\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 1z8e4n5m with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 1z8e4n5m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/1z8e4n5m\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/1z8e4n5m</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/1z8e4n5m\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjF6OGU0bjVtOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175238-1z8e4n5m/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175238-1z8e4n5m/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175238-1z8e4n5m/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175238-1z8e4n5m/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175238-1z8e4n5m/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175238-1z8e4n5m/media/graph/graph_0_summary_017f2ad7.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175238-1z8e4n5m/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175238-1z8e4n5m/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175238-1z8e4n5m/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.227613762952387\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.5639856949241625\n",
            "The running loss is:\n",
            "17.898133425042033\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.6628938305571124\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175238-1z8e4n5m/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175238-1z8e4n5m/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.654380347579718\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.5797918647251747\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175238-1z8e4n5m/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175238-1z8e4n5m/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.89348728954792\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.3664254551684415\n",
            "1\n",
            "The running loss is:\n",
            "8.431944162817672\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.31229422825250636\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175238-1z8e4n5m/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175238-1z8e4n5m/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.609166414942592\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.31885801536824415\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['1z8e4n5m']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.016866168007255\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.3339580062224909\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175238-1z8e4n5m/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175238-1z8e4n5m/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.0558643322438\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.29836534563865924\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175238-1z8e4n5m/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175238-1z8e4n5m/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.685117572546005\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.3216710212054076\n",
            "1\n",
            "The running loss is:\n",
            "6.745234100148082\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.2498234851906697\n",
            "Data saved to: \n",
            "28_May_202005_52PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175238-1z8e4n5m/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175238-1z8e4n5m/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_52PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  783.742432\n",
            "66  2020-04-22  sub_region  ...             66  782.695801\n",
            "67  2020-04-23  sub_region  ...             67  783.955322\n",
            "68  2020-04-24  sub_region  ...             68  783.325439\n",
            "69  2020-04-25  sub_region  ...             69  783.187744\n",
            "70  2020-04-26  sub_region  ...             70  783.111938\n",
            "71  2020-04-27  sub_region  ...             71  783.345886\n",
            "72  2020-04-28  sub_region  ...             72  782.962280\n",
            "73  2020-04-29  sub_region  ...             73  782.751831\n",
            "74  2020-04-30  sub_region  ...             74  783.127808\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175238-1z8e4n5m/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175238-1z8e4n5m/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 1z8e4n5m \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175238-1z8e4n5m/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175238-1z8e4n5m/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175238-1z8e4n5m/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175238-1z8e4n5m/media/plotly/test_plot_20_86f33ef2.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175238-1z8e4n5m/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175238-1z8e4n5m/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175238-1z8e4n5m/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175238-1z8e4n5m/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 1z8e4n5m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Network error resolved after 0:00:15.324762, resuming normal operation.\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: zv56nmxw with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: zv56nmxw\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/zv56nmxw\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/zv56nmxw</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/zv56nmxw\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnp2NTZubXh3OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175309-zv56nmxw/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175309-zv56nmxw/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175309-zv56nmxw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175309-zv56nmxw/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175309-zv56nmxw/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175309-zv56nmxw/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175309-zv56nmxw/media/graph/graph_0_summary_aa917372.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175309-zv56nmxw/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175309-zv56nmxw/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.714700505137444\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.7967192501975939\n",
            "The running loss is:\n",
            "16.891354955732822\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.6496674982974162\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175309-zv56nmxw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175309-zv56nmxw/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.139072712510824\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.620733565865801\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175309-zv56nmxw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175309-zv56nmxw/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.367559241130948\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.5910599708127288\n",
            "2\n",
            "The running loss is:\n",
            "14.148938469588757\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.5441899411380291\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_53PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175309-zv56nmxw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175309-zv56nmxw/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_53PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  440.134735\n",
            "66  2020-04-22  sub_region  ...             66  439.800903\n",
            "67  2020-04-23  sub_region  ...             67  443.283325\n",
            "68  2020-04-24  sub_region  ...             68  442.842438\n",
            "69  2020-04-25  sub_region  ...             69  440.653381\n",
            "70  2020-04-26  sub_region  ...             70  441.242767\n",
            "71  2020-04-27  sub_region  ...             71  440.577118\n",
            "72  2020-04-28  sub_region  ...             72  442.682800\n",
            "73  2020-04-29  sub_region  ...             73  441.382904\n",
            "74  2020-04-30  sub_region  ...             74  445.453522\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['zv56nmxw']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175309-zv56nmxw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175309-zv56nmxw/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175309-zv56nmxw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175309-zv56nmxw/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175309-zv56nmxw/media/plotly/test_plot_10_0d49373a.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175309-zv56nmxw/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175309-zv56nmxw/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: zv56nmxw \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175309-zv56nmxw/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175309-zv56nmxw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175309-zv56nmxw/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175309-zv56nmxw/media/plotly/test_plot_all_11_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175309-zv56nmxw/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175309-zv56nmxw/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: zv56nmxw\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: htbwvcg1 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: htbwvcg1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/htbwvcg1\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/htbwvcg1</a><br/>\n",
              "            "
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            "DEBUG:wandb.meta:probe for git information\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/htbwvcg1\n",
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            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmh0Ynd2Y2cxOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
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          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
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          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175321-htbwvcg1/config.yaml\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175321-htbwvcg1/media/graph/graph_0_summary_41ed4eef.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175321-htbwvcg1/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175321-htbwvcg1/requirements.txt\n",
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          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "23.482627481222153\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.9031779800470059\n",
            "The running loss is:\n",
            "20.23513476550579\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.778274414057915\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175321-htbwvcg1/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.649361565709114\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.8326677525272737\n",
            "The running loss is:\n",
            "15.995433270931244\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175321-htbwvcg1/wandb-history.jsonl\n"
          ],
          "name": "stderr"
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          "output_type": "stream",
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            "0.615208971958894\n"
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          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175321-htbwvcg1/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "12.781472019851208\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.4915950776865849\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175321-htbwvcg1/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175321-htbwvcg1/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.850842893123627\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.3404170343509087\n",
            "The running loss is:\n",
            "6.8902468085289\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.26500949263572693\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['htbwvcg1']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175321-htbwvcg1/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175321-htbwvcg1/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.579151637852192\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.32996737068662274\n",
            "2\n",
            "The running loss is:\n",
            "6.014725340530276\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.23133559002039525\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175321-htbwvcg1/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175321-htbwvcg1/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.920796798542142\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.18926141532854393\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_53PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175321-htbwvcg1/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175321-htbwvcg1/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_53PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175321-htbwvcg1/config.yaml\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  784.844604\n",
            "66  2020-04-22  sub_region  ...             66  785.061829\n",
            "67  2020-04-23  sub_region  ...             67  785.951660\n",
            "68  2020-04-24  sub_region  ...             68  785.422729\n",
            "69  2020-04-25  sub_region  ...             69  784.747925\n",
            "70  2020-04-26  sub_region  ...             70  784.533691\n",
            "71  2020-04-27  sub_region  ...             71  784.408447\n",
            "72  2020-04-28  sub_region  ...             72  785.171936\n",
            "73  2020-04-29  sub_region  ...             73  784.649170\n",
            "74  2020-04-30  sub_region  ...             74  785.943665\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175321-htbwvcg1/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175321-htbwvcg1/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175321-htbwvcg1/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175321-htbwvcg1/media/plotly/test_plot_20_bf33d510.plotly.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175321-htbwvcg1/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175321-htbwvcg1/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: htbwvcg1 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175321-htbwvcg1/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175321-htbwvcg1/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175321-htbwvcg1/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175321-htbwvcg1/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175321-htbwvcg1/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175321-htbwvcg1/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: htbwvcg1\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: frnme9lu with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: frnme9lu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/frnme9lu\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/frnme9lu</a><br/>\n",
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          "metadata": {
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/frnme9lu\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmZybm1lOWx1OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175338-frnme9lu/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175338-frnme9lu/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175338-frnme9lu/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175338-frnme9lu/media/graph/graph_0_summary_649639ab.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175338-frnme9lu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175338-frnme9lu/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175338-frnme9lu/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175338-frnme9lu/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175338-frnme9lu/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "23.31422958523035\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.896701137893475\n",
            "The running loss is:\n",
            "19.628748193383217\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.7549518535916622\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175338-frnme9lu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175338-frnme9lu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.03840383887291\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.8476309168797272\n",
            "The running loss is:\n",
            "15.539160393178463\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.5976600151222485\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175338-frnme9lu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175338-frnme9lu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "12.818177118897438\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.4930068122652861\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175338-frnme9lu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175338-frnme9lu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.63734956085682\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.4091288292637238\n",
            "1\n",
            "The running loss is:\n",
            "6.949803344905376\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.2673001286502068\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['frnme9lu']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175338-frnme9lu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175338-frnme9lu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.64347056671977\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.29397963718152964\n",
            "1\n",
            "The running loss is:\n",
            "11.507175385951996\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.4425836686904614\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175338-frnme9lu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175338-frnme9lu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.667488571256399\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.21798032966370767\n",
            "Data saved to: \n",
            "28_May_202005_53PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_53PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175338-frnme9lu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175338-frnme9lu/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175338-frnme9lu/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  749.537842\n",
            "66  2020-04-22  sub_region  ...             66  753.229736\n",
            "67  2020-04-23  sub_region  ...             67  753.515015\n",
            "68  2020-04-24  sub_region  ...             68  753.205444\n",
            "69  2020-04-25  sub_region  ...             69  751.122925\n",
            "70  2020-04-26  sub_region  ...             70  750.473755\n",
            "71  2020-04-27  sub_region  ...             71  748.162842\n",
            "72  2020-04-28  sub_region  ...             72  752.245361\n",
            "73  2020-04-29  sub_region  ...             73  750.474243\n",
            "74  2020-04-30  sub_region  ...             74  754.320679\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175338-frnme9lu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175338-frnme9lu/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175338-frnme9lu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175338-frnme9lu/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175338-frnme9lu/media/plotly/test_plot_20_67e12e5e.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175338-frnme9lu/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175338-frnme9lu/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
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          "output_type": "stream",
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            "wandb: Agent Finished Run: frnme9lu \n",
            "\n"
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            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175338-frnme9lu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175338-frnme9lu/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175338-frnme9lu/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175338-frnme9lu/media/plotly\n",
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            "INFO:wandb.wandb_agent:Cleaning up finished run: frnme9lu\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
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          "text": [
            "wandb: Agent Starting Run: 69e2yc72 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 69e2yc72\n"
          ],
          "name": "stdout"
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          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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          "name": "stderr"
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        {
          "output_type": "display_data",
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              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/69e2yc72\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/69e2yc72</a><br/>\n",
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        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175354-69e2yc72/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175354-69e2yc72/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175354-69e2yc72/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175354-69e2yc72/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175354-69e2yc72/wandb-metadata.json\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175354-69e2yc72/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175354-69e2yc72/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.830005079507828\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.7132002031803131\n",
            "The running loss is:\n",
            "16.9639795422554\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.6785591816902161\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175354-69e2yc72/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175354-69e2yc72/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.417406290769577\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.776696251630783\n",
            "The running loss is:\n",
            "13.737709395587444\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.5495083758234978\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175354-69e2yc72/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175354-69e2yc72/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.103170596063137\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.5241268238425255\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175354-69e2yc72/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175354-69e2yc72/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.996175795793533\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.3998470318317413\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_53PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_53PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['69e2yc72']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175354-69e2yc72/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175354-69e2yc72/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175354-69e2yc72/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
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            "67  2020-04-23  sub_region  ...             67  420.017090\n",
            "68  2020-04-24  sub_region  ...             68  420.572571\n",
            "69  2020-04-25  sub_region  ...             69  419.899597\n",
            "70  2020-04-26  sub_region  ...             70  419.641113\n",
            "71  2020-04-27  sub_region  ...             71  419.378143\n",
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            "73  2020-04-29  sub_region  ...             73  419.175232\n",
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            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175354-69e2yc72/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175354-69e2yc72/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175354-69e2yc72/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175354-69e2yc72/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175354-69e2yc72/media/plotly/test_plot_12_d17e53a2.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175354-69e2yc72/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175354-69e2yc72/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 69e2yc72 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175354-69e2yc72/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175354-69e2yc72/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175354-69e2yc72/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175354-69e2yc72/media/plotly/test_plot_all_13_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175354-69e2yc72/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175354-69e2yc72/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 69e2yc72\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 23bl3fhm with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 23bl3fhm\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/23bl3fhm\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/23bl3fhm</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/23bl3fhm\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjIzYmwzZmhtOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/media/graph/graph_0_summary_16c8da2c.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.741086795926094\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.7096434718370438\n",
            "The running loss is:\n",
            "16.730679839849472\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.6692271935939789\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.939407482743263\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.7975762993097305\n",
            "The running loss is:\n",
            "13.255669929087162\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.5302267971634865\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "11.027616888284683\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.4411046755313873\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.718288138508797\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.26873152554035185\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['23bl3fhm']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.313029149547219\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.29252116598188876\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.773601979017258\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.5109440791606903\n",
            "The running loss is:\n",
            "6.80379575304687\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.2721518301218748\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.739170776680112\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.18956683106720448\n",
            "2\n",
            "Data saved to: \n",
            "28_May_202005_54PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_54PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  770.853760\n",
            "66  2020-04-22  sub_region  ...             66  769.976440\n",
            "67  2020-04-23  sub_region  ...             67  771.220154\n",
            "68  2020-04-24  sub_region  ...             68  770.911987\n",
            "69  2020-04-25  sub_region  ...             69  770.517700\n",
            "70  2020-04-26  sub_region  ...             70  770.186768\n",
            "71  2020-04-27  sub_region  ...             71  770.193970\n",
            "72  2020-04-28  sub_region  ...             72  770.157593\n",
            "73  2020-04-29  sub_region  ...             73  770.232788\n",
            "74  2020-04-30  sub_region  ...             74  770.960083\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/media/plotly/test_plot_20_57ec849c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 23bl3fhm \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175411-23bl3fhm/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 23bl3fhm\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: vslypx7u with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: vslypx7u\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/vslypx7u\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/vslypx7u</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/vslypx7u\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnZzbHlweDd1OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/media/graph/graph_0_summary_775214d5.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.64892402663827\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.5055157046903063\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "37.692751033231616\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.3960278160456154\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.47567129507661\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7583581961139485\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.933722829446197\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.7753230677572666\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['vslypx7u']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.798208009451628\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.7703040003500603\n",
            "The running loss is:\n",
            "20.184429235756397\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.7475714531761629\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "20.411993622779846\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.755999763806661\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.94804622977972\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.7388165270288786\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_54PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_54PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  474.087341\n",
            "66  2020-04-22  sub_region  ...             66  474.107361\n",
            "67  2020-04-23  sub_region  ...             67  473.755005\n",
            "68  2020-04-24  sub_region  ...             68  474.003113\n",
            "69  2020-04-25  sub_region  ...             69  474.199402\n",
            "70  2020-04-26  sub_region  ...             70  474.221405\n",
            "71  2020-04-27  sub_region  ...             71  474.191833\n",
            "72  2020-04-28  sub_region  ...             72  473.996887\n",
            "73  2020-04-29  sub_region  ...             73  474.185181\n",
            "74  2020-04-30  sub_region  ...             74  473.697998\n",
            "\n",
            "[21 rows x 28 columns]"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/media/plotly/test_plot_16_1f8d7214.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/media\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: vslypx7u \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/media/plotly/test_plot_all_17_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175427-vslypx7u/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: vslypx7u\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 8eb1h9re with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 8eb1h9re\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/8eb1h9re\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/8eb1h9re</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/8eb1h9re\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjhlYjFoOXJlOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/media/graph/graph_0_summary_eaee8e05.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.37478191871196\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.49536229328562814\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "37.45775743201375\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.3873243493338425\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.595209177583456\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7627855250956835\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.006036780774593\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.7780013622509109\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['8eb1h9re']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.73020259477198\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.7677852812878512\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.24301341921091\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.7497412377485523\n",
            "1\n",
            "The running loss is:\n",
            "20.167157787829638\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.7469317699196162\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "2\n",
            "The running loss is:\n",
            "18.895125970244408\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.6998194803794225\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_54PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_54PM_model.pth\n",
            "interpolate should be below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  474.395355\n",
            "66  2020-04-22  sub_region  ...             66  474.474243\n",
            "67  2020-04-23  sub_region  ...             67  473.946503\n",
            "68  2020-04-24  sub_region  ...             68  474.251740\n",
            "69  2020-04-25  sub_region  ...             69  474.498688\n",
            "70  2020-04-26  sub_region  ...             70  474.531128\n",
            "71  2020-04-27  sub_region  ...             71  474.499969\n",
            "72  2020-04-28  sub_region  ...             72  474.257050\n",
            "73  2020-04-29  sub_region  ...             73  474.527222\n",
            "74  2020-04-30  sub_region  ...             74  473.818420\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/media/plotly/test_plot_16_c257944a.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 8eb1h9re \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/media/plotly/test_plot_all_17_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175444-8eb1h9re/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 8eb1h9re\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: u5disclf with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: u5disclf\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/u5disclf\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/u5disclf</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/u5disclf\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnU1ZGlzY2xmOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175501-u5disclf/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175501-u5disclf/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175501-u5disclf/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175501-u5disclf/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175501-u5disclf/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175501-u5disclf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175501-u5disclf/media/graph/graph_0_summary_6c6e54e0.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175501-u5disclf/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175501-u5disclf/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.137208757922053\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.5606373614045205\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175501-u5disclf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175501-u5disclf/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "30.88287841156125\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.1438103115393057\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175501-u5disclf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175501-u5disclf/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "25.371903955936432\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.9397001465161642\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175501-u5disclf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175501-u5disclf/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.891498629003763\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.7737592084816208\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['u5disclf']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175501-u5disclf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175501-u5disclf/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.58536821603775\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.7624210450384352\n",
            "1\n",
            "The running loss is:\n",
            "21.16692252457142\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.7839600935026452\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175501-u5disclf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175501-u5disclf/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.775313809514046\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.7694560670190387\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_55PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175501-u5disclf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175501-u5disclf/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_55PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  422.063049\n",
            "66  2020-04-22  sub_region  ...             66  422.065277\n",
            "67  2020-04-23  sub_region  ...             67  422.008148\n",
            "68  2020-04-24  sub_region  ...             68  422.044128\n",
            "69  2020-04-25  sub_region  ...             69  422.075989\n",
            "70  2020-04-26  sub_region  ...             70  422.085114\n",
            "71  2020-04-27  sub_region  ...             71  422.084900\n",
            "72  2020-04-28  sub_region  ...             72  422.078888\n",
            "73  2020-04-29  sub_region  ...             73  422.060028\n",
            "74  2020-04-30  sub_region  ...             74  422.037537\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175501-u5disclf/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175501-u5disclf/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: u5disclf \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175501-u5disclf/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175501-u5disclf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175501-u5disclf/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175501-u5disclf/media/plotly/test_plot_all_15_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175501-u5disclf/media/plotly/test_plot_14_7a77c25e.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175501-u5disclf/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175501-u5disclf/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175501-u5disclf/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: u5disclf\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: zpwwfeyr with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: zpwwfeyr\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/zpwwfeyr\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/zpwwfeyr</a><br/>\n",
              "            "
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            "DEBUG:wandb.meta:probe for git information\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/zpwwfeyr\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
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            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnpwd3dmZXlyOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
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          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175519-zpwwfeyr/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175519-zpwwfeyr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175519-zpwwfeyr/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175519-zpwwfeyr/wandb-metadata.json\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175519-zpwwfeyr/requirements.txt\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.071377269923687\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.5581991581453217\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175519-zpwwfeyr/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175519-zpwwfeyr/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "31.568736284971237\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.1692124549989347\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175519-zpwwfeyr/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175519-zpwwfeyr/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "25.198651179671288\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.9332833770248625\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175519-zpwwfeyr/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175519-zpwwfeyr/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.102718703448772\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.7815821742018064\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['zpwwfeyr']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175519-zpwwfeyr/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175519-zpwwfeyr/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.57193885743618\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.7619236613865252\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175519-zpwwfeyr/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175519-zpwwfeyr/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.114420972764492\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.7820155915838701\n",
            "2\n",
            "The running loss is:\n",
            "21.067522056400776\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.7802785946815102\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175519-zpwwfeyr/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175519-zpwwfeyr/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_55PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_55PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175519-zpwwfeyr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175519-zpwwfeyr/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  423.483063\n",
            "66  2020-04-22  sub_region  ...             66  423.511169\n",
            "67  2020-04-23  sub_region  ...             67  423.472900\n",
            "68  2020-04-24  sub_region  ...             68  423.498932\n",
            "69  2020-04-25  sub_region  ...             69  423.520294\n",
            "70  2020-04-26  sub_region  ...             70  423.524902\n",
            "71  2020-04-27  sub_region  ...             71  423.520996\n",
            "72  2020-04-28  sub_region  ...             72  423.510651\n",
            "73  2020-04-29  sub_region  ...             73  423.502411\n",
            "74  2020-04-30  sub_region  ...             74  423.487152\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175519-zpwwfeyr/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175519-zpwwfeyr/config.yaml\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175519-zpwwfeyr/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175519-zpwwfeyr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175519-zpwwfeyr/media/plotly/test_plot_14_0bb7c432.plotly.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175519-zpwwfeyr/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175519-zpwwfeyr/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: zpwwfeyr \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175519-zpwwfeyr/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175519-zpwwfeyr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175519-zpwwfeyr/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175519-zpwwfeyr/media/plotly/test_plot_all_15_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175519-zpwwfeyr/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175519-zpwwfeyr/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: zpwwfeyr\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: b6hcpq3h with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: b6hcpq3h\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/b6hcpq3h\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/b6hcpq3h</a><br/>\n",
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          "metadata": {
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        {
          "output_type": "stream",
          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/b6hcpq3h\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmI2aGNwcTNoOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/media/graph/graph_0_summary_13555ea6.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "24.238217145204544\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.9322391209694055\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.11776690930128\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.6199141118962032\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.947837613523006\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.6518399082124233\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.64614214003086\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.6786977746165715\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['b6hcpq3h']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.301386021077633\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.6269763854260628\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.042810605838895\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.6170311771476498\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.744363751262426\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.6055524519716318\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.016306111589074\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.6160117735226567\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.813427444547415\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.6082087478672082\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.24589281156659\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.6248420312140996\n",
            "Data saved to: \n",
            "28_May_202005_55PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_55PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  509.934052\n",
            "66  2020-04-22  sub_region  ...             66  507.839417\n",
            "67  2020-04-23  sub_region  ...             67  509.408234\n",
            "68  2020-04-24  sub_region  ...             68  509.694031\n",
            "69  2020-04-25  sub_region  ...             69  509.575317\n",
            "70  2020-04-26  sub_region  ...             70  508.830750\n",
            "71  2020-04-27  sub_region  ...             71  509.065674\n",
            "72  2020-04-28  sub_region  ...             72  507.174622\n",
            "73  2020-04-29  sub_region  ...             73  509.588074\n",
            "74  2020-04-30  sub_region  ...             74  507.856110\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/media/plotly/test_plot_20_75179c3f.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: b6hcpq3h \n",
            "\n"
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          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175540-b6hcpq3h/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: b6hcpq3h\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: ryhzse7k with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: ryhzse7k\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/ryhzse7k\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/ryhzse7k</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/ryhzse7k\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnJ5aHpzZTdrOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175604-ryhzse7k/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175604-ryhzse7k/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175604-ryhzse7k/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175604-ryhzse7k/media/graph/graph_0_summary_24bc68c5.graph.json\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175604-ryhzse7k/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175604-ryhzse7k/wandb-summary.json\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "24.46081606298685\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.9408006178071866\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175604-ryhzse7k/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175604-ryhzse7k/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.266936726868153\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.625651412571852\n",
            "1\n"
          ],
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        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175604-ryhzse7k/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175604-ryhzse7k/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.894336327910423\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.6497821664580932\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175604-ryhzse7k/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175604-ryhzse7k/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.256735399365425\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.6637205922832856\n"
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          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['ryhzse7k']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175604-ryhzse7k/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175604-ryhzse7k/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.231802832335234\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.6243001089359705\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175604-ryhzse7k/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.1003286074847\n"
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          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175604-ryhzse7k/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.6192434079801807\n",
            "The running loss is:\n",
            "15.810061607509851\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.6080792925965327\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175604-ryhzse7k/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175604-ryhzse7k/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.048187194392085\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.6172379690150802\n",
            "2\n"
          ],
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        },
        {
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          "text": [
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175604-ryhzse7k/wandb-summary.json\n"
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          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.809576485306025\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.6080606340502317\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_56PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175604-ryhzse7k/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175604-ryhzse7k/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_56PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  529.040771\n",
            "66  2020-04-22  sub_region  ...             66  528.143555\n",
            "67  2020-04-23  sub_region  ...             67  528.838806\n",
            "68  2020-04-24  sub_region  ...             68  528.961365\n",
            "69  2020-04-25  sub_region  ...             69  528.801025\n",
            "70  2020-04-26  sub_region  ...             70  528.515015\n",
            "71  2020-04-27  sub_region  ...             71  528.581177\n",
            "72  2020-04-28  sub_region  ...             72  528.077698\n",
            "73  2020-04-29  sub_region  ...             73  528.772461\n",
            "74  2020-04-30  sub_region  ...             74  528.409302\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175604-ryhzse7k/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175604-ryhzse7k/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: ryhzse7k \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175604-ryhzse7k/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175604-ryhzse7k/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175604-ryhzse7k/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175604-ryhzse7k/media/plotly/test_plot_18_712b0439.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175604-ryhzse7k/media/plotly/test_plot_all_19_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175604-ryhzse7k/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175604-ryhzse7k/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175604-ryhzse7k/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: ryhzse7k\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 17i2vd1z with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 17i2vd1z\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/17i2vd1z\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/17i2vd1z</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/17i2vd1z\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjE3aTJ2ZDF6OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175626-17i2vd1z/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175626-17i2vd1z/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175626-17i2vd1z/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175626-17i2vd1z/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175626-17i2vd1z/media/graph/graph_0_summary_3528a6ee.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175626-17i2vd1z/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175626-17i2vd1z/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175626-17i2vd1z/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175626-17i2vd1z/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "24.921816915273666\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.958531419818218\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175626-17i2vd1z/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175626-17i2vd1z/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.33100463449955\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.858884793634598\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175626-17i2vd1z/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175626-17i2vd1z/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.428308714181185\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.7857041813146609\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175626-17i2vd1z/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175626-17i2vd1z/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.427796185016632\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.6702998532698705\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['17i2vd1z']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.2829820625484\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.6647300793287846\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175626-17i2vd1z/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175626-17i2vd1z/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.887216325849295\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.6110467817634344\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_56PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175626-17i2vd1z/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175626-17i2vd1z/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_56PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  559.779724\n",
            "66  2020-04-22  sub_region  ...             66  560.009094\n",
            "67  2020-04-23  sub_region  ...             67  560.145813\n",
            "68  2020-04-24  sub_region  ...             68  560.165466\n",
            "69  2020-04-25  sub_region  ...             69  560.001709\n",
            "70  2020-04-26  sub_region  ...             70  560.061584\n",
            "71  2020-04-27  sub_region  ...             71  559.992859\n",
            "72  2020-04-28  sub_region  ...             72  560.228088\n",
            "73  2020-04-29  sub_region  ...             73  559.984314\n",
            "74  2020-04-30  sub_region  ...             74  560.468567\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175626-17i2vd1z/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175626-17i2vd1z/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175626-17i2vd1z/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175626-17i2vd1z/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175626-17i2vd1z/media/plotly/test_plot_12_f72c3f2f.plotly.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175626-17i2vd1z/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175626-17i2vd1z/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 17i2vd1z \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175626-17i2vd1z/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175626-17i2vd1z/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175626-17i2vd1z/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175626-17i2vd1z/media/plotly/test_plot_all_13_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175626-17i2vd1z/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175626-17i2vd1z/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 17i2vd1z\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: b81wjoof with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: b81wjoof\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/b81wjoof\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/b81wjoof</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/b81wjoof\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmI4MXdqb29mOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175642-b81wjoof/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175642-b81wjoof/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175642-b81wjoof/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175642-b81wjoof/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175642-b81wjoof/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175642-b81wjoof/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175642-b81wjoof/media/graph/graph_0_summary_cba168a9.graph.json\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175642-b81wjoof/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "25.268363758921623\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.9718601445739086\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175642-b81wjoof/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175642-b81wjoof/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.410943418741226\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.8619593622592779\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175642-b81wjoof/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175642-b81wjoof/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.349964793771505\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.7826909536065964\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175642-b81wjoof/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175642-b81wjoof/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.21460761502385\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.6621002928855327\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['b81wjoof']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.72370159626007\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.6816808306253873\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175642-b81wjoof/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175642-b81wjoof/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.540141738951206\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.597697759190431\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_56PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175642-b81wjoof/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175642-b81wjoof/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_56PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  559.067444\n",
            "66  2020-04-22  sub_region  ...             66  559.143799\n",
            "67  2020-04-23  sub_region  ...             67  559.442505\n",
            "68  2020-04-24  sub_region  ...             68  559.442566\n",
            "69  2020-04-25  sub_region  ...             69  559.225464\n",
            "70  2020-04-26  sub_region  ...             70  559.264404\n",
            "71  2020-04-27  sub_region  ...             71  559.198975\n",
            "72  2020-04-28  sub_region  ...             72  559.425171\n",
            "73  2020-04-29  sub_region  ...             73  559.358643\n",
            "74  2020-04-30  sub_region  ...             74  559.806274\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175642-b81wjoof/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175642-b81wjoof/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: b81wjoof \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175642-b81wjoof/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175642-b81wjoof/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175642-b81wjoof/media/plotly/test_plot_12_b8d9f787.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175642-b81wjoof/media/plotly/test_plot_all_13_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175642-b81wjoof/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175642-b81wjoof/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175642-b81wjoof/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175642-b81wjoof/wandb-events.jsonl\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: b81wjoof\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 2kziz1z8 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 2kziz1z8\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/2kziz1z8\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/2kziz1z8</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/2kziz1z8\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
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            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/media/graph/graph_0_summary_d6549107.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.4773468375206\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.819093873500824\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.37137946486473\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.7748551785945892\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.317528434097767\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.6527011373639107\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.260967433452606\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.5704386973381043\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['2kziz1z8']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.17140232771635\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.566856093108654\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.054600022733212\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.5621840009093284\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.07607826590538\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.5630431306362152\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.025826334953308\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.5610330533981324\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.500305883586407\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.5400122353434562\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.7346723228693\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.509386892914772\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_57PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_57PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/config.yaml\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/wandb-history.jsonl\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  384.512299\n",
            "66  2020-04-22  sub_region  ...             66  383.916504\n",
            "67  2020-04-23  sub_region  ...             67  384.334045\n",
            "68  2020-04-24  sub_region  ...             68  384.549500\n",
            "69  2020-04-25  sub_region  ...             69  384.431915\n",
            "70  2020-04-26  sub_region  ...             70  384.146332\n",
            "71  2020-04-27  sub_region  ...             71  384.198578\n",
            "72  2020-04-28  sub_region  ...             72  383.738586\n",
            "73  2020-04-29  sub_region  ...             73  384.381348\n",
            "74  2020-04-30  sub_region  ...             74  383.990662\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/media/plotly/test_plot_20_1987c841.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 2kziz1z8 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175659-2kziz1z8/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 2kziz1z8\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: jz1lf7ky with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: jz1lf7ky\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/jz1lf7ky\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/jz1lf7ky</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/jz1lf7ky\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmp6MWxmN2t5OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/media/graph/graph_0_summary_ff747659.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.519908547401428\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.8207963418960571\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.348501980304718\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.7739400792121888\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.020245015621185\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.6408098006248474\n",
            "The running loss is:\n",
            "14.293965496122837\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.5717586198449135\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['jz1lf7ky']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.129456043243408\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.5651782417297363\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.794427633285522\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.5517771053314209\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.02604092657566\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.5210416370630264\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.591085441410542\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.3836434176564217\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.496750168502331\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.29987000674009323\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_57PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_57PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  388.992981\n",
            "66  2020-04-22  sub_region  ...             66  387.531372\n",
            "67  2020-04-23  sub_region  ...             67  388.493744\n",
            "68  2020-04-24  sub_region  ...             68  388.772583\n",
            "69  2020-04-25  sub_region  ...             69  388.547852\n",
            "70  2020-04-26  sub_region  ...             70  387.934692\n",
            "71  2020-04-27  sub_region  ...             71  388.074402\n",
            "72  2020-04-28  sub_region  ...             72  387.295563\n",
            "73  2020-04-29  sub_region  ...             73  388.444427\n",
            "74  2020-04-30  sub_region  ...             74  387.736328\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/media/plotly/test_plot_18_166901d4.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: jz1lf7ky \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/media/plotly/test_plot_all_19_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175716-jz1lf7ky/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: jz1lf7ky\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: s4fonam9 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: s4fonam9\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/s4fonam9\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/s4fonam9</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/s4fonam9\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnM0Zm9uYW05OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "11.178941723890603\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.4140348786626149\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175732-s4fonam9/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175732-s4fonam9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175732-s4fonam9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175732-s4fonam9/media/graph/graph_0_summary_1f3837d4.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175732-s4fonam9/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175732-s4fonam9/wandb-metadata.json\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.482956442050636\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.5364057941500235\n",
            "1\n",
            "The running loss is:\n",
            "12.16446726070717\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.4505358244706359\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175732-s4fonam9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175732-s4fonam9/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.8048704912635\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.2890692774542037\n",
            "1\n",
            "The running loss is:\n",
            "7.099390174262226\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.26294037682452687\n",
            "The running loss is:\n",
            "6.51896614592988\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.24144319058999558\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175732-s4fonam9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175732-s4fonam9/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.50308578943077\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.24085502923817667\n",
            "1\n",
            "The running loss is:\n",
            "6.709017640678212\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.24848213483993378\n",
            "The running loss is:\n",
            "6.639082251727814\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.2458919352491783\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175732-s4fonam9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175732-s4fonam9/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.743373372781207\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.21271753232522989\n",
            "Data saved to: \n",
            "28_May_202005_57PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.wandb_agent:Running runs: ['s4fonam9']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_57PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175732-s4fonam9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175732-s4fonam9/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175732-s4fonam9/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  758.606506\n",
            "66  2020-04-22  sub_region  ...             66  838.690002\n",
            "67  2020-04-23  sub_region  ...             67  739.158081\n",
            "68  2020-04-24  sub_region  ...             68  760.182251\n",
            "69  2020-04-25  sub_region  ...             69  791.689392\n",
            "70  2020-04-26  sub_region  ...             70  821.668945\n",
            "71  2020-04-27  sub_region  ...             71  813.191467\n",
            "72  2020-04-28  sub_region  ...             72  824.506714\n",
            "73  2020-04-29  sub_region  ...             73  810.240173\n",
            "74  2020-04-30  sub_region  ...             74  760.039307\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175732-s4fonam9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175732-s4fonam9/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175732-s4fonam9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175732-s4fonam9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175732-s4fonam9/media/plotly/test_plot_20_50b5ba08.plotly.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175732-s4fonam9/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175732-s4fonam9/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: s4fonam9 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175732-s4fonam9/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175732-s4fonam9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175732-s4fonam9/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175732-s4fonam9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175732-s4fonam9/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175732-s4fonam9/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: s4fonam9\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: ay7p9npe with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: ay7p9npe\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/ay7p9npe\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/ay7p9npe</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/ay7p9npe\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmF5N3A5bnBlOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175749-ay7p9npe/config.yaml\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175749-ay7p9npe/wandb-metadata.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175749-ay7p9npe/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175749-ay7p9npe/wandb-events.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175749-ay7p9npe/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "11.243638909421861\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.41643107071932817\n",
            "The running loss is:\n",
            "15.065432488219813\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.5579789810451783\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175749-ay7p9npe/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175749-ay7p9npe/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175749-ay7p9npe/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175749-ay7p9npe/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175749-ay7p9npe/media/graph/graph_0_summary_dec9c9e3.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175749-ay7p9npe/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175749-ay7p9npe/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.927668149583042\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.36769141294752006\n",
            "The running loss is:\n",
            "7.617937269620597\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.2821458248007629\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175749-ay7p9npe/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175749-ay7p9npe/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.723564739339054\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.2490209162718168\n",
            "The running loss is:\n",
            "7.1353028768789954\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.2642704769214443\n",
            "1\n",
            "The running loss is:\n",
            "5.726659771054983\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.21209851003907346\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175749-ay7p9npe/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175749-ay7p9npe/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.842962785856798\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.2534430661428444\n",
            "The running loss is:\n",
            "6.147264284198172\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.22767645497030267\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['ay7p9npe']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.772955868189456\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.25085021734035023\n",
            "Data saved to: \n",
            "28_May_202005_57PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175749-ay7p9npe/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175749-ay7p9npe/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_57PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175749-ay7p9npe/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  702.101074\n",
            "66  2020-04-22  sub_region  ...             66  799.573730\n",
            "67  2020-04-23  sub_region  ...             67  688.857788\n",
            "68  2020-04-24  sub_region  ...             68  695.529663\n",
            "69  2020-04-25  sub_region  ...             69  724.923462\n",
            "70  2020-04-26  sub_region  ...             70  761.772095\n",
            "71  2020-04-27  sub_region  ...             71  748.695679\n",
            "72  2020-04-28  sub_region  ...             72  803.100220\n",
            "73  2020-04-29  sub_region  ...             73  733.273804\n",
            "74  2020-04-30  sub_region  ...             74  718.350464\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175749-ay7p9npe/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175749-ay7p9npe/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175749-ay7p9npe/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175749-ay7p9npe/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175749-ay7p9npe/media/plotly/test_plot_20_e049cd21.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175749-ay7p9npe/media\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175749-ay7p9npe/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: ay7p9npe \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175749-ay7p9npe/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175749-ay7p9npe/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175749-ay7p9npe/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175749-ay7p9npe/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175749-ay7p9npe/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175749-ay7p9npe/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: ay7p9npe\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: xhybkiq8 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: xhybkiq8\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/xhybkiq8\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/xhybkiq8</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/xhybkiq8\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnhoeWJraXE4OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "13.754779417067766\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.5094362747062136\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175806-xhybkiq8/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175806-xhybkiq8/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175806-xhybkiq8/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175806-xhybkiq8/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175806-xhybkiq8/media/graph/graph_0_summary_27ea2e80.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175806-xhybkiq8/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175806-xhybkiq8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175806-xhybkiq8/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175806-xhybkiq8/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.335549898445606\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.5679833295720594\n",
            "1\n",
            "The running loss is:\n",
            "12.792411483824253\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.4737930179194168\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175806-xhybkiq8/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175806-xhybkiq8/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.463905839249492\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.38755206812035153\n",
            "1\n",
            "The running loss is:\n",
            "8.178016487509012\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.3028894995373708\n",
            "The running loss is:\n",
            "7.196833549998701\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.2665493907406926\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175806-xhybkiq8/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175806-xhybkiq8/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.724783644080162\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.2861030979288949\n",
            "2\n",
            "The running loss is:\n",
            "7.328360717743635\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.27142076732383835\n",
            "The running loss is:\n",
            "8.112628399394453\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.30046771849609083\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175806-xhybkiq8/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175806-xhybkiq8/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.612887730821967\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.244921767808221\n",
            "Data saved to: \n",
            "28_May_202005_58PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.wandb_agent:Running runs: ['xhybkiq8']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_58PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  785.347290\n",
            "66  2020-04-22  sub_region  ...             66  848.155396\n",
            "67  2020-04-23  sub_region  ...             67  778.021973\n",
            "68  2020-04-24  sub_region  ...             68  802.694946\n",
            "69  2020-04-25  sub_region  ...             69  814.782837\n",
            "70  2020-04-26  sub_region  ...             70  827.907227\n",
            "71  2020-04-27  sub_region  ...             71  817.994751\n",
            "72  2020-04-28  sub_region  ...             72  834.097107\n",
            "73  2020-04-29  sub_region  ...             73  812.438049\n",
            "74  2020-04-30  sub_region  ...             74  797.486328\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175806-xhybkiq8/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175806-xhybkiq8/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175806-xhybkiq8/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175806-xhybkiq8/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175806-xhybkiq8/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175806-xhybkiq8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175806-xhybkiq8/media/plotly/test_plot_20_e9ff783e.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175806-xhybkiq8/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175806-xhybkiq8/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: xhybkiq8 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175806-xhybkiq8/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175806-xhybkiq8/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175806-xhybkiq8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175806-xhybkiq8/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175806-xhybkiq8/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175806-xhybkiq8/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: xhybkiq8\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 2bp9e1u9 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 2bp9e1u9\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/2bp9e1u9\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/2bp9e1u9</a><br/>\n",
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          "metadata": {
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        {
          "output_type": "stream",
          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/2bp9e1u9\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjJicDllMXU5OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "13.334896691143513\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.4938850626349449\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175822-2bp9e1u9/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175822-2bp9e1u9/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175822-2bp9e1u9/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175822-2bp9e1u9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175822-2bp9e1u9/media/graph/graph_0_summary_eab20644.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175822-2bp9e1u9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175822-2bp9e1u9/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175822-2bp9e1u9/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175822-2bp9e1u9/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.006994970142841\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.5558146285238089\n",
            "1\n",
            "The running loss is:\n",
            "11.964153863489628\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.44311680975887513\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175822-2bp9e1u9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175822-2bp9e1u9/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.46366179548204\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.3505059924252607\n",
            "1\n",
            "The running loss is:\n",
            "7.935978587716818\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.29392513287840066\n",
            "The running loss is:\n",
            "7.160205790773034\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.26519280706566795\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175822-2bp9e1u9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175822-2bp9e1u9/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.432532018050551\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.2752789636315019\n",
            "2\n",
            "The running loss is:\n",
            "6.930170862004161\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.256672994889043\n",
            "The running loss is:\n",
            "7.583688434679061\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.2808773494325578\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175822-2bp9e1u9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175822-2bp9e1u9/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.852206656709313\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.2537854317299746\n",
            "Data saved to: \n",
            "28_May_202005_58PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_58PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['2bp9e1u9']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175822-2bp9e1u9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175822-2bp9e1u9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175822-2bp9e1u9/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  792.838745\n",
            "66  2020-04-22  sub_region  ...             66  839.469238\n",
            "67  2020-04-23  sub_region  ...             67  793.361755\n",
            "68  2020-04-24  sub_region  ...             68  806.429504\n",
            "69  2020-04-25  sub_region  ...             69  816.609619\n",
            "70  2020-04-26  sub_region  ...             70  828.707764\n",
            "71  2020-04-27  sub_region  ...             71  823.823608\n",
            "72  2020-04-28  sub_region  ...             72  829.279419\n",
            "73  2020-04-29  sub_region  ...             73  820.153503\n",
            "74  2020-04-30  sub_region  ...             74  806.989746\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175822-2bp9e1u9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175822-2bp9e1u9/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175822-2bp9e1u9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175822-2bp9e1u9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175822-2bp9e1u9/media/plotly/test_plot_20_4bc95361.plotly.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175822-2bp9e1u9/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175822-2bp9e1u9/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 2bp9e1u9 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175822-2bp9e1u9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175822-2bp9e1u9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175822-2bp9e1u9/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175822-2bp9e1u9/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175822-2bp9e1u9/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175822-2bp9e1u9/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 2bp9e1u9\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: izbxufpu with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: izbxufpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/izbxufpu\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/izbxufpu</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/izbxufpu\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOml6Ynh1ZnB1OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "18.989742126315832\n",
            "The number of items in train is: \n",
            "26\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175839-izbxufpu/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The loss for epoch 0\n",
            "0.7303746971659936\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175839-izbxufpu/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175839-izbxufpu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175839-izbxufpu/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175839-izbxufpu/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175839-izbxufpu/media/graph/graph_0_summary_0b88f687.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175839-izbxufpu/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175839-izbxufpu/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175839-izbxufpu/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.38419210910797\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.6301612349656912\n",
            "1\n",
            "The running loss is:\n",
            "15.279926925897598\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.5876894971499076\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175839-izbxufpu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175839-izbxufpu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.945733822882175\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.4209897624185452\n",
            "1\n",
            "The running loss is:\n",
            "5.606289468705654\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.21562651802714056\n",
            "2\n",
            "The running loss is:\n",
            "4.256948810070753\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.16372880038733667\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175839-izbxufpu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175839-izbxufpu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.631639575585723\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.17813998367637396\n",
            "1\n",
            "The running loss is:\n",
            "6.9152334509417415\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.26597051734391314\n",
            "The running loss is:\n",
            "3.967709585092962\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.15260421481126776\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175839-izbxufpu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175839-izbxufpu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "3.5715523255057633\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.13736739713483706\n",
            "Data saved to: \n",
            "28_May_202005_58PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.wandb_agent:Running runs: ['izbxufpu']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_58PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175839-izbxufpu/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175839-izbxufpu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175839-izbxufpu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  814.696899\n",
            "66  2020-04-22  sub_region  ...             66  825.249207\n",
            "67  2020-04-23  sub_region  ...             67  843.944824\n",
            "68  2020-04-24  sub_region  ...             68  816.804077\n",
            "69  2020-04-25  sub_region  ...             69  796.358887\n",
            "70  2020-04-26  sub_region  ...             70  799.779358\n",
            "71  2020-04-27  sub_region  ...             71  801.697021\n",
            "72  2020-04-28  sub_region  ...             72  800.930298\n",
            "73  2020-04-29  sub_region  ...             73  815.445740\n",
            "74  2020-04-30  sub_region  ...             74  822.305115\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175839-izbxufpu/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175839-izbxufpu/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175839-izbxufpu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175839-izbxufpu/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175839-izbxufpu/media/plotly/test_plot_20_d82f34c9.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175839-izbxufpu/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175839-izbxufpu/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: izbxufpu \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175839-izbxufpu/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175839-izbxufpu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175839-izbxufpu/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175839-izbxufpu/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175839-izbxufpu/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175839-izbxufpu/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: izbxufpu\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: uuquf6mj with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: uuquf6mj\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/uuquf6mj\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/uuquf6mj</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/uuquf6mj\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnV1cXVmNm1qOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "18.905425552278757\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.7271317520107214\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175851-uuquf6mj/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175851-uuquf6mj/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175851-uuquf6mj/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175851-uuquf6mj/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175851-uuquf6mj/media/graph/graph_0_summary_b0526367.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175851-uuquf6mj/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175851-uuquf6mj/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175851-uuquf6mj/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175851-uuquf6mj/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.324342384934425\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.6278593224974779\n",
            "1\n",
            "The running loss is:\n",
            "15.281524695456028\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.5877509498252318\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175851-uuquf6mj/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175851-uuquf6mj/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.686566263437271\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.41102177936297196\n",
            "1\n",
            "The running loss is:\n",
            "6.059355471283197\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.2330521335108922\n",
            "The running loss is:\n",
            "4.697019984945655\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.1806546148056021\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175851-uuquf6mj/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175851-uuquf6mj/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.977002476342022\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.26834624909007776\n",
            "1\n",
            "The running loss is:\n",
            "5.771942357998341\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.2219977829999362\n",
            "The running loss is:\n",
            "4.5645317947492\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.17555891518266156\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175851-uuquf6mj/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175851-uuquf6mj/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.686972144991159\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.21872969788427538\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_58PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_58PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['uuquf6mj']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175851-uuquf6mj/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175851-uuquf6mj/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175851-uuquf6mj/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  736.851746\n",
            "66  2020-04-22  sub_region  ...             66  772.893066\n",
            "67  2020-04-23  sub_region  ...             67  762.027588\n",
            "68  2020-04-24  sub_region  ...             68  752.332520\n",
            "69  2020-04-25  sub_region  ...             69  736.720947\n",
            "70  2020-04-26  sub_region  ...             70  741.464417\n",
            "71  2020-04-27  sub_region  ...             71  733.462769\n",
            "72  2020-04-28  sub_region  ...             72  760.639526\n",
            "73  2020-04-29  sub_region  ...             73  746.799805\n",
            "74  2020-04-30  sub_region  ...             74  770.807861\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175851-uuquf6mj/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175851-uuquf6mj/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175851-uuquf6mj/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175851-uuquf6mj/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175851-uuquf6mj/media/plotly/test_plot_20_556d85cf.plotly.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175851-uuquf6mj/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175851-uuquf6mj/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: uuquf6mj \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175851-uuquf6mj/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175851-uuquf6mj/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175851-uuquf6mj/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175851-uuquf6mj/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175851-uuquf6mj/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175851-uuquf6mj/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: uuquf6mj\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: gbl3pckk with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: gbl3pckk\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/gbl3pckk\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/gbl3pckk</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/gbl3pckk\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmdibDNwY2trOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "16.948153391480446\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.6518520535184786\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175902-gbl3pckk/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175902-gbl3pckk/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175902-gbl3pckk/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175902-gbl3pckk/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175902-gbl3pckk/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175902-gbl3pckk/media/graph/graph_0_summary_cf8f90f5.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175902-gbl3pckk/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175902-gbl3pckk/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175902-gbl3pckk/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.85379159078002\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.5328381381069238\n",
            "The running loss is:\n",
            "9.64835274219513\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.37109049008442807\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175902-gbl3pckk/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175902-gbl3pckk/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.4732926171273\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.3258958698895115\n",
            "The running loss is:\n",
            "5.7244476936757565\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.22017106514137524\n",
            "The running loss is:\n",
            "6.5513798370957375\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.25197614758060527\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175902-gbl3pckk/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175902-gbl3pckk/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.37942417897284\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.3222855453451092\n",
            "The running loss is:\n",
            "4.463485406711698\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.1716725156427576\n",
            "1\n",
            "The running loss is:\n",
            "4.722438246943057\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.18163224026704064\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175902-gbl3pckk/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175902-gbl3pckk/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.340588949620724\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.1669457288315663\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202005_59PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_59PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['gbl3pckk']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  851.006592\n",
            "66  2020-04-22  sub_region  ...             66  861.678711\n",
            "67  2020-04-23  sub_region  ...             67  870.574524\n",
            "68  2020-04-24  sub_region  ...             68  854.424133\n",
            "69  2020-04-25  sub_region  ...             69  835.877502\n",
            "70  2020-04-26  sub_region  ...             70  840.583374\n",
            "71  2020-04-27  sub_region  ...             71  839.167969\n",
            "72  2020-04-28  sub_region  ...             72  850.335999\n",
            "73  2020-04-29  sub_region  ...             73  848.938354\n",
            "74  2020-04-30  sub_region  ...             74  859.935547\n",
            "\n",
            "[21 rows x 28 columns]"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175902-gbl3pckk/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175902-gbl3pckk/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175902-gbl3pckk/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175902-gbl3pckk/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175902-gbl3pckk/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175902-gbl3pckk/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175902-gbl3pckk/media/plotly/test_plot_20_0f4d1d99.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175902-gbl3pckk/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175902-gbl3pckk/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: gbl3pckk \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175902-gbl3pckk/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175902-gbl3pckk/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175902-gbl3pckk/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175902-gbl3pckk/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175902-gbl3pckk/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175902-gbl3pckk/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: gbl3pckk\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: blhbm0pa with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: blhbm0pa\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/blhbm0pa\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/blhbm0pa</a><br/>\n",
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            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
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          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
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          "output_type": "stream",
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "16.634709760546684\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.6397965292517955\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175914-blhbm0pa/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175914-blhbm0pa/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175914-blhbm0pa/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175914-blhbm0pa/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175914-blhbm0pa/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175914-blhbm0pa/media/graph/graph_0_summary_ce932c35.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175914-blhbm0pa/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175914-blhbm0pa/media/graph\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.594469293951988\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.5613257420750765\n",
            "The running loss is:\n",
            "9.017393708229065\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.3468228349318871\n",
            "1\n",
            "The running loss is:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175914-blhbm0pa/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "7.2338755913078785\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175914-blhbm0pa/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.27822598428107226\n",
            "The running loss is:\n",
            "4.978128891438246\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.19146649582454792\n",
            "1\n",
            "The running loss is:\n",
            "5.498803533613682\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.21149244360052621\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175914-blhbm0pa/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175914-blhbm0pa/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.14033218100667\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.27462816080794883\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_59PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_59PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175914-blhbm0pa/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175914-blhbm0pa/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175914-blhbm0pa/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  736.526611\n",
            "66  2020-04-22  sub_region  ...             66  758.064575\n",
            "67  2020-04-23  sub_region  ...             67  751.773193\n",
            "68  2020-04-24  sub_region  ...             68  746.766174\n",
            "69  2020-04-25  sub_region  ...             69  738.920166\n",
            "70  2020-04-26  sub_region  ...             70  743.997314\n",
            "71  2020-04-27  sub_region  ...             71  741.321045\n",
            "72  2020-04-28  sub_region  ...             72  750.091064\n",
            "73  2020-04-29  sub_region  ...             73  743.901367\n",
            "74  2020-04-30  sub_region  ...             74  754.618958\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['blhbm0pa']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175914-blhbm0pa/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175914-blhbm0pa/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175914-blhbm0pa/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175914-blhbm0pa/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175914-blhbm0pa/media/plotly/test_plot_14_251cab71.plotly.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175914-blhbm0pa/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175914-blhbm0pa/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: blhbm0pa \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175914-blhbm0pa/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175914-blhbm0pa/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175914-blhbm0pa/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175914-blhbm0pa/media/plotly/test_plot_all_15_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175914-blhbm0pa/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175914-blhbm0pa/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: blhbm0pa\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 9lg5on60 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 9lg5on60\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/9lg5on60\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/9lg5on60</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/9lg5on60\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjlsZzVvbjYwOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "14.528097435832024\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.581123897433281\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175925-9lg5on60/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175925-9lg5on60/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175925-9lg5on60/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175925-9lg5on60/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175925-9lg5on60/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175925-9lg5on60/media/graph/graph_0_summary_fa07a0d1.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175925-9lg5on60/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175925-9lg5on60/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175925-9lg5on60/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.321749061346054\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.5728699624538421\n",
            "The running loss is:\n",
            "8.53690830618143\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.3414763322472572\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175925-9lg5on60/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175925-9lg5on60/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "3.877267986536026\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.15509071946144104\n",
            "The running loss is:\n",
            "3.9804319692775607\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.15921727877110242\n",
            "1\n",
            "The running loss is:\n",
            "4.088011069223285\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.16352044276893138\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175925-9lg5on60/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175925-9lg5on60/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "3.642200954724103\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.14568803818896414\n",
            "1\n",
            "The running loss is:\n",
            "4.692892727442086\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.18771570909768343\n",
            "2\n",
            "The running loss is:\n",
            "3.2474756240844727\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.1298990249633789\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175925-9lg5on60/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175925-9lg5on60/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "3.19531544810161\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.1278126179240644\n",
            "Data saved to: \n",
            "28_May_202005_59PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_59PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['9lg5on60']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175925-9lg5on60/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175925-9lg5on60/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175925-9lg5on60/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  711.413086\n",
            "66  2020-04-22  sub_region  ...             66  714.489502\n",
            "67  2020-04-23  sub_region  ...             67  749.420166\n",
            "68  2020-04-24  sub_region  ...             68  730.427063\n",
            "69  2020-04-25  sub_region  ...             69  707.012146\n",
            "70  2020-04-26  sub_region  ...             70  700.846252\n",
            "71  2020-04-27  sub_region  ...             71  701.634644\n",
            "72  2020-04-28  sub_region  ...             72  702.439697\n",
            "73  2020-04-29  sub_region  ...             73  710.740356\n",
            "74  2020-04-30  sub_region  ...             74  736.382812\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175925-9lg5on60/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175925-9lg5on60/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175925-9lg5on60/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175925-9lg5on60/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175925-9lg5on60/media/plotly/test_plot_20_b4b65440.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175925-9lg5on60/media\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175925-9lg5on60/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 9lg5on60 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175925-9lg5on60/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175925-9lg5on60/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175925-9lg5on60/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175925-9lg5on60/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175925-9lg5on60/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175925-9lg5on60/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 9lg5on60\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: l04ebxec with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: l04ebxec\n"
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          "name": "stdout"
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            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/l04ebxec\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/l04ebxec</a><br/>\n",
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            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
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          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "14.583468616008759\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.5833387446403503\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175942-l04ebxec/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175942-l04ebxec/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175942-l04ebxec/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175942-l04ebxec/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175942-l04ebxec/media/graph/graph_0_summary_099d89b2.graph.json\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175942-l04ebxec/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175942-l04ebxec/media/graph\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.234198205173016\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.4493679282069206\n",
            "1\n",
            "The running loss is:\n",
            "6.8342317044734955\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.2733692681789398\n",
            "2\n",
            "The running loss is:\n",
            "4.914771635085344\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.19659086540341378\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175942-l04ebxec/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175942-l04ebxec/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.097113943658769\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.24388455774635076\n",
            "1\n",
            "The running loss is:\n",
            "5.855831284075975\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.23423325136303902\n",
            "2\n",
            "The running loss is:\n",
            "4.823610462248325\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.19294441848993302\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175942-l04ebxec/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175942-l04ebxec/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.116255223751068\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.2046502089500427\n",
            "1\n",
            "The running loss is:\n",
            "3.9354080138728023\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.1574163205549121\n",
            "2\n",
            "The running loss is:\n",
            "3.635712749324739\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.14542850997298956\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175942-l04ebxec/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175942-l04ebxec/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202005_59PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202005_59PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['l04ebxec']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175942-l04ebxec/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175942-l04ebxec/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175942-l04ebxec/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  742.793396\n",
            "66  2020-04-22  sub_region  ...             66  736.652344\n",
            "67  2020-04-23  sub_region  ...             67  754.830688\n",
            "68  2020-04-24  sub_region  ...             68  749.173950\n",
            "69  2020-04-25  sub_region  ...             69  738.829346\n",
            "70  2020-04-26  sub_region  ...             70  734.617004\n",
            "71  2020-04-27  sub_region  ...             71  732.794556\n",
            "72  2020-04-28  sub_region  ...             72  736.974976\n",
            "73  2020-04-29  sub_region  ...             73  737.531616\n",
            "74  2020-04-30  sub_region  ...             74  753.737549\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175942-l04ebxec/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175942-l04ebxec/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175942-l04ebxec/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175942-l04ebxec/media/plotly/test_plot_20_71421788.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175942-l04ebxec/media\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175942-l04ebxec/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: l04ebxec \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175942-l04ebxec/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175942-l04ebxec/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175942-l04ebxec/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175942-l04ebxec/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175942-l04ebxec/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175942-l04ebxec/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: l04ebxec\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: sp9yc18q with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: sp9yc18q\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/sp9yc18q\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/sp9yc18q</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/sp9yc18q\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnNwOXljMThxOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175959-sp9yc18q/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175959-sp9yc18q/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175959-sp9yc18q/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175959-sp9yc18q/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175959-sp9yc18q/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175959-sp9yc18q/media/graph/graph_0_summary_c09f855e.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175959-sp9yc18q/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175959-sp9yc18q/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175959-sp9yc18q/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.349453534625354\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.4573871679490872\n",
            "The running loss is:\n",
            "30.535183822968975\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.130932734184036\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175959-sp9yc18q/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175959-sp9yc18q/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.304419979453087\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.6779414807204847\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175959-sp9yc18q/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175959-sp9yc18q/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.870923589682207\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.4026267996178595\n",
            "2\n",
            "The running loss is:\n",
            "9.898105231113732\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.36659649004124933\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_00PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175959-sp9yc18q/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175959-sp9yc18q/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_00PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "INFO:wandb.wandb_agent:Running runs: ['sp9yc18q']\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  502.529541\n",
            "66  2020-04-22  sub_region  ...             66  492.594849\n",
            "67  2020-04-23  sub_region  ...             67  496.524628\n",
            "68  2020-04-24  sub_region  ...             68  495.569824\n",
            "69  2020-04-25  sub_region  ...             69  495.434570\n",
            "70  2020-04-26  sub_region  ...             70  493.173859\n",
            "71  2020-04-27  sub_region  ...             71  493.890808\n",
            "72  2020-04-28  sub_region  ...             72  491.183136\n",
            "73  2020-04-29  sub_region  ...             73  497.434570\n",
            "74  2020-04-30  sub_region  ...             74  489.901245\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175959-sp9yc18q/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175959-sp9yc18q/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: sp9yc18q \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175959-sp9yc18q/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175959-sp9yc18q/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175959-sp9yc18q/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175959-sp9yc18q/media/plotly/test_plot_10_c557f1bd.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175959-sp9yc18q/media/plotly/test_plot_all_11_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175959-sp9yc18q/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175959-sp9yc18q/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_175959-sp9yc18q/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: sp9yc18q\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: rf529106 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: rf529106\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/rf529106\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/rf529106</a><br/>\n",
              "            "
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/rf529106\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnJmNTI5MTA2OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180011-rf529106/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180011-rf529106/media/graph/graph_0_summary_0a959aa4.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180011-rf529106/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180011-rf529106/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180011-rf529106/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180011-rf529106/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180011-rf529106/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180011-rf529106/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180011-rf529106/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.00125639885664\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.4444909777354311\n",
            "The running loss is:\n",
            "27.564760353183374\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.0209170501179028\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180011-rf529106/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180011-rf529106/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.1493139838567\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7092338512539519\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180011-rf529106/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180011-rf529106/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.482985021779314\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.3512216674733079\n",
            "The running loss is:\n",
            "8.379846808500588\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.31036469661113286\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180011-rf529106/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180011-rf529106/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.564052293615532\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.24311304791168636\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['rf529106']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.043116731874761\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.2608561752546208\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180011-rf529106/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180011-rf529106/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.5104550529504195\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.2411279649240896\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180011-rf529106/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180011-rf529106/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.045392430853099\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.2609404604019666\n",
            "The running loss is:\n",
            "6.926817309111357\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.25654878922634655\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202006_00PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180011-rf529106/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180011-rf529106/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_00PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180011-rf529106/config.yaml\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  778.043945\n",
            "66  2020-04-22  sub_region  ...             66  783.455933\n",
            "67  2020-04-23  sub_region  ...             67  766.487671\n",
            "68  2020-04-24  sub_region  ...             68  773.625916\n",
            "69  2020-04-25  sub_region  ...             69  782.676147\n",
            "70  2020-04-26  sub_region  ...             70  784.909546\n",
            "71  2020-04-27  sub_region  ...             71  784.748901\n",
            "72  2020-04-28  sub_region  ...             72  778.614258\n",
            "73  2020-04-29  sub_region  ...             73  782.117676\n",
            "74  2020-04-30  sub_region  ...             74  761.960938\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180011-rf529106/wandb-summary.json\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180011-rf529106/wandb-summary.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180011-rf529106/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180011-rf529106/media/plotly/test_plot_20_647b6c30.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180011-rf529106/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180011-rf529106/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: rf529106 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180011-rf529106/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180011-rf529106/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180011-rf529106/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180011-rf529106/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180011-rf529106/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180011-rf529106/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: rf529106\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: eupck2gp with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: eupck2gp\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/eupck2gp\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/eupck2gp</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/eupck2gp\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmV1cGNrMmdwOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180028-eupck2gp/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180028-eupck2gp/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180028-eupck2gp/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180028-eupck2gp/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180028-eupck2gp/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180028-eupck2gp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180028-eupck2gp/media/graph/graph_0_summary_0ad3907c.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180028-eupck2gp/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180028-eupck2gp/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.362042212858796\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.5689645264021777\n",
            "The running loss is:\n",
            "18.604257106781006\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.6890465595104076\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180028-eupck2gp/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180028-eupck2gp/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.387704892084002\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7180631441512594\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180028-eupck2gp/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180028-eupck2gp/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.115502156317234\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.6339074872710087\n",
            "The running loss is:\n",
            "9.399747041985393\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.3481387793327923\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180028-eupck2gp/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180028-eupck2gp/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.062234118580818\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.2986012636511414\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['eupck2gp']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.508644045330584\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.2780979276048364\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180028-eupck2gp/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180028-eupck2gp/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.624856340698898\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.2824020866925518\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180028-eupck2gp/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180028-eupck2gp/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.4580579325556755\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.27622436787243243\n",
            "The running loss is:\n",
            "6.958448266610503\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.25772030617075936\n",
            "Data saved to: \n",
            "28_May_202006_00PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180028-eupck2gp/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180028-eupck2gp/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_00PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  772.555298\n",
            "66  2020-04-22  sub_region  ...             66  766.406433\n",
            "67  2020-04-23  sub_region  ...             67  773.479492\n",
            "68  2020-04-24  sub_region  ...             68  770.044922\n",
            "69  2020-04-25  sub_region  ...             69  772.980347\n",
            "70  2020-04-26  sub_region  ...             70  772.595215\n",
            "71  2020-04-27  sub_region  ...             71  772.935425\n",
            "72  2020-04-28  sub_region  ...             72  770.757202\n",
            "73  2020-04-29  sub_region  ...             73  772.154602\n",
            "74  2020-04-30  sub_region  ...             74  771.334900\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180028-eupck2gp/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180028-eupck2gp/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180028-eupck2gp/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180028-eupck2gp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180028-eupck2gp/media/plotly/test_plot_20_cf3ed598.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180028-eupck2gp/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180028-eupck2gp/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180028-eupck2gp/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180028-eupck2gp/wandb-summary.json\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180028-eupck2gp/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: eupck2gp\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
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          "text": [
            "wandb: Agent Starting Run: o5ynrlxu with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: o5ynrlxu\n"
          ],
          "name": "stdout"
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          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/o5ynrlxu\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/o5ynrlxu</a><br/>\n",
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          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
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          "output_type": "stream",
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180044-o5ynrlxu/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180044-o5ynrlxu/media/graph/graph_0_summary_44fabac8.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180044-o5ynrlxu/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180044-o5ynrlxu/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180044-o5ynrlxu/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180044-o5ynrlxu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180044-o5ynrlxu/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180044-o5ynrlxu/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180044-o5ynrlxu/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.227613762952387\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.5639856949241625\n",
            "The running loss is:\n",
            "17.898133425042033\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.6628938305571124\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180044-o5ynrlxu/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180044-o5ynrlxu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.654380347579718\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.5797918647251747\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180044-o5ynrlxu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180044-o5ynrlxu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.89348728954792\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.3664254551684415\n",
            "1\n",
            "The running loss is:\n",
            "8.431944162817672\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.31229422825250636\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180044-o5ynrlxu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180044-o5ynrlxu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.609166414942592\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.31885801536824415\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['o5ynrlxu']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180044-o5ynrlxu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180044-o5ynrlxu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.016866168007255\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.3339580062224909\n",
            "2\n",
            "The running loss is:\n",
            "8.0558643322438\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.29836534563865924\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180044-o5ynrlxu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180044-o5ynrlxu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.685117572546005\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.3216710212054076\n",
            "1\n",
            "The running loss is:\n",
            "6.745234100148082\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.2498234851906697\n",
            "Data saved to: \n",
            "28_May_202006_00PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180044-o5ynrlxu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180044-o5ynrlxu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_00PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  783.742432\n",
            "66  2020-04-22  sub_region  ...             66  782.695801\n",
            "67  2020-04-23  sub_region  ...             67  783.955322\n",
            "68  2020-04-24  sub_region  ...             68  783.325439\n",
            "69  2020-04-25  sub_region  ...             69  783.187744\n",
            "70  2020-04-26  sub_region  ...             70  783.111938\n",
            "71  2020-04-27  sub_region  ...             71  783.345886\n",
            "72  2020-04-28  sub_region  ...             72  782.962280\n",
            "73  2020-04-29  sub_region  ...             73  782.751831\n",
            "74  2020-04-30  sub_region  ...             74  783.127808\n",
            "\n",
            "[21 rows x 28 columns]"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180044-o5ynrlxu/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
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          "name": "stdout"
        },
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          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180044-o5ynrlxu/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180044-o5ynrlxu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180044-o5ynrlxu/wandb-history.jsonl\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180044-o5ynrlxu/media/plotly/test_plot_20_86f33ef2.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180044-o5ynrlxu/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180044-o5ynrlxu/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: o5ynrlxu \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180044-o5ynrlxu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180044-o5ynrlxu/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180044-o5ynrlxu/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180044-o5ynrlxu/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180044-o5ynrlxu/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180044-o5ynrlxu/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: o5ynrlxu\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: c6rdxmat with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: c6rdxmat\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/c6rdxmat\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/c6rdxmat</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/c6rdxmat\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmM2cmR4bWF0OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/media/graph/graph_0_summary_bf741941.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.012039236724377\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.8081553552586299\n",
            "The running loss is:\n",
            "16.833910040557384\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.6474580784829763\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.23337048664689\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.6243604033325727\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.538650223985314\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.5976403932302043\n",
            "1\n",
            "The running loss is:\n",
            "15.044064596295357\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.5786178690882829\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.831363804638386\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.4550524540245533\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['c6rdxmat']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.703582746908069\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.2962916441118488\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.853043738752604\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.3405016822597155\n",
            "2\n",
            "The running loss is:\n",
            "5.49346605874598\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.21128715610561463\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.049623145721853\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.23267781329699433\n",
            "Data saved to: \n",
            "28_May_202006_01PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_01PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  793.348389\n",
            "66  2020-04-22  sub_region  ...             66  793.042908\n",
            "67  2020-04-23  sub_region  ...             67  790.546631\n",
            "68  2020-04-24  sub_region  ...             68  791.214233\n",
            "69  2020-04-25  sub_region  ...             69  792.006836\n",
            "70  2020-04-26  sub_region  ...             70  792.608032\n",
            "71  2020-04-27  sub_region  ...             71  792.963989\n",
            "72  2020-04-28  sub_region  ...             72  789.634521\n",
            "73  2020-04-29  sub_region  ...             73  793.287476\n",
            "74  2020-04-30  sub_region  ...             74  785.882202\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/media/plotly/test_plot_20_ef160644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: c6rdxmat \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180101-c6rdxmat/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: c6rdxmat\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: nnrubgkf with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: nnrubgkf\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/nnrubgkf\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/nnrubgkf</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/nnrubgkf\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOm5ucnViZ2tmOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180118-nnrubgkf/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180118-nnrubgkf/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180118-nnrubgkf/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180118-nnrubgkf/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180118-nnrubgkf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180118-nnrubgkf/media/graph/graph_0_summary_c4d34699.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180118-nnrubgkf/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180118-nnrubgkf/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180118-nnrubgkf/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.714700505137444\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.7967192501975939\n",
            "The running loss is:\n",
            "16.891354955732822\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.6496674982974162\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180118-nnrubgkf/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180118-nnrubgkf/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.139072712510824\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.620733565865801\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180118-nnrubgkf/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180118-nnrubgkf/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.367559241130948\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.5910599708127288\n",
            "2\n",
            "The running loss is:\n",
            "14.148938469588757\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.5441899411380291\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_01PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180118-nnrubgkf/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180118-nnrubgkf/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_01PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['nnrubgkf']\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  440.134735\n",
            "66  2020-04-22  sub_region  ...             66  439.800903\n",
            "67  2020-04-23  sub_region  ...             67  443.283325\n",
            "68  2020-04-24  sub_region  ...             68  442.842438\n",
            "69  2020-04-25  sub_region  ...             69  440.653381\n",
            "70  2020-04-26  sub_region  ...             70  441.242767\n",
            "71  2020-04-27  sub_region  ...             71  440.577118\n",
            "72  2020-04-28  sub_region  ...             72  442.682800\n",
            "73  2020-04-29  sub_region  ...             73  441.382904\n",
            "74  2020-04-30  sub_region  ...             74  445.453522\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180118-nnrubgkf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180118-nnrubgkf/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: nnrubgkf \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180118-nnrubgkf/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180118-nnrubgkf/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180118-nnrubgkf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180118-nnrubgkf/media/plotly/test_plot_10_0d49373a.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180118-nnrubgkf/media/plotly/test_plot_all_11_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180118-nnrubgkf/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180118-nnrubgkf/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180118-nnrubgkf/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: nnrubgkf\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: auxmbkll with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: auxmbkll\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/auxmbkll\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/auxmbkll</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/auxmbkll\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmF1eG1ia2xsOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180132-auxmbkll/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180132-auxmbkll/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180132-auxmbkll/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180132-auxmbkll/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180132-auxmbkll/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180132-auxmbkll/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180132-auxmbkll/media/graph/graph_0_summary_61198c9f.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180132-auxmbkll/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180132-auxmbkll/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "23.482627481222153\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.9031779800470059\n",
            "The running loss is:\n",
            "20.23513476550579\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.778274414057915\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180132-auxmbkll/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180132-auxmbkll/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.649361565709114\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.8326677525272737\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180132-auxmbkll/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180132-auxmbkll/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.995433270931244\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.615208971958894\n",
            "1\n",
            "The running loss is:\n",
            "12.781472019851208\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.4915950776865849\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180132-auxmbkll/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180132-auxmbkll/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.850842893123627\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.3404170343509087\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['auxmbkll']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.8902468085289\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.26500949263572693\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180132-auxmbkll/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180132-auxmbkll/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.579151637852192\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.32996737068662274\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180132-auxmbkll/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180132-auxmbkll/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.014725340530276\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.23133559002039525\n",
            "The running loss is:\n",
            "4.920796798542142\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.18926141532854393\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202006_01PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180132-auxmbkll/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180132-auxmbkll/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_01PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  784.844604\n",
            "66  2020-04-22  sub_region  ...             66  785.061829\n",
            "67  2020-04-23  sub_region  ...             67  785.951660\n",
            "68  2020-04-24  sub_region  ...             68  785.422729\n",
            "69  2020-04-25  sub_region  ...             69  784.747925\n",
            "70  2020-04-26  sub_region  ...             70  784.533691\n",
            "71  2020-04-27  sub_region  ...             71  784.408447\n",
            "72  2020-04-28  sub_region  ...             72  785.171936\n",
            "73  2020-04-29  sub_region  ...             73  784.649170\n",
            "74  2020-04-30  sub_region  ...             74  785.943665\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180132-auxmbkll/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180132-auxmbkll/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: auxmbkll \n",
            "\n"
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          "output_type": "stream",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180132-auxmbkll/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180132-auxmbkll/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180132-auxmbkll/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180132-auxmbkll/media/plotly/test_plot_20_bf33d510.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180132-auxmbkll/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180132-auxmbkll/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180132-auxmbkll/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: auxmbkll\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: srhmp681 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: srhmp681\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/srhmp681\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/srhmp681</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/srhmp681\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnNyaG1wNjgxOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
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            "INFO:wandb.run_manager:saving pip packages\n",
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        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
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        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180149-srhmp681/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180149-srhmp681/media/graph/graph_0_summary_dded512a.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180149-srhmp681/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180149-srhmp681/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180149-srhmp681/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180149-srhmp681/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180149-srhmp681/requirements.txt\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "23.31422958523035\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.896701137893475\n",
            "The running loss is:\n",
            "19.628748193383217\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.7549518535916622\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180149-srhmp681/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180149-srhmp681/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.03840383887291\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.8476309168797272\n",
            "The running loss is:\n",
            "15.539160393178463\n",
            "The number of items in train is: \n",
            "26\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180149-srhmp681/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The loss for epoch 3\n",
            "0.5976600151222485\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180149-srhmp681/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "12.818177118897438\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.4930068122652861\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180149-srhmp681/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180149-srhmp681/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.63734956085682\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.4091288292637238\n",
            "1\n",
            "The running loss is:\n",
            "6.949803344905376\n",
            "The number of items in train is: \n",
            "26\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['srhmp681']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The loss for epoch 6\n",
            "0.2673001286502068\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180149-srhmp681/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180149-srhmp681/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.64347056671977\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.29397963718152964\n",
            "1\n",
            "The running loss is:\n",
            "11.507175385951996\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.4425836686904614\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180149-srhmp681/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180149-srhmp681/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.667488571256399\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.21798032966370767\n",
            "Data saved to: \n",
            "28_May_202006_01PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
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          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_01PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180149-srhmp681/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180149-srhmp681/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180149-srhmp681/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  749.537842\n",
            "66  2020-04-22  sub_region  ...             66  753.229736\n",
            "67  2020-04-23  sub_region  ...             67  753.515015\n",
            "68  2020-04-24  sub_region  ...             68  753.205444\n",
            "69  2020-04-25  sub_region  ...             69  751.122925\n",
            "70  2020-04-26  sub_region  ...             70  750.473755\n",
            "71  2020-04-27  sub_region  ...             71  748.162842\n",
            "72  2020-04-28  sub_region  ...             72  752.245361\n",
            "73  2020-04-29  sub_region  ...             73  750.474243\n",
            "74  2020-04-30  sub_region  ...             74  754.320679\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180149-srhmp681/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180149-srhmp681/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: srhmp681 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180149-srhmp681/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180149-srhmp681/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180149-srhmp681/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180149-srhmp681/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180149-srhmp681/media/plotly/test_plot_20_67e12e5e.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180149-srhmp681/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180149-srhmp681/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180149-srhmp681/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: srhmp681\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: n3ev68is with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: n3ev68is\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/n3ev68is\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/n3ev68is</a><br/>\n",
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          "metadata": {
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        {
          "output_type": "stream",
          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/n3ev68is\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOm4zZXY2OGlzOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180205-n3ev68is/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180205-n3ev68is/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180205-n3ev68is/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180205-n3ev68is/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180205-n3ev68is/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180205-n3ev68is/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180205-n3ev68is/media/graph/graph_0_summary_ab0d380c.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180205-n3ev68is/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180205-n3ev68is/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.830005079507828\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.7132002031803131\n",
            "The running loss is:\n",
            "16.9639795422554\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.6785591816902161\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180205-n3ev68is/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180205-n3ev68is/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.417406290769577\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.776696251630783\n",
            "The running loss is:\n",
            "13.737709395587444\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.5495083758234978\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180205-n3ev68is/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180205-n3ev68is/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "13.103170596063137\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.5241268238425255\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180205-n3ev68is/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180205-n3ev68is/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.996175795793533\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.3998470318317413\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_02PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_02PM_model.pth\n",
            "interpolate should be below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['n3ev68is']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  417.858063\n",
            "66  2020-04-22  sub_region  ...             66  419.026245\n",
            "67  2020-04-23  sub_region  ...             67  420.017090\n",
            "68  2020-04-24  sub_region  ...             68  420.572571\n",
            "69  2020-04-25  sub_region  ...             69  419.899597\n",
            "70  2020-04-26  sub_region  ...             70  419.641113\n",
            "71  2020-04-27  sub_region  ...             71  419.378143\n",
            "72  2020-04-28  sub_region  ...             72  419.695251\n",
            "73  2020-04-29  sub_region  ...             73  419.175232\n",
            "74  2020-04-30  sub_region  ...             74  421.516815\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180205-n3ev68is/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180205-n3ev68is/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180205-n3ev68is/wandb-history.jsonl\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180205-n3ev68is/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180205-n3ev68is/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180205-n3ev68is/media/plotly/test_plot_12_d17e53a2.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180205-n3ev68is/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180205-n3ev68is/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: n3ev68is \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180205-n3ev68is/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180205-n3ev68is/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180205-n3ev68is/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180205-n3ev68is/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180205-n3ev68is/media/plotly/test_plot_all_13_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180205-n3ev68is/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: n3ev68is\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: yrue8x18 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: yrue8x18\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/yrue8x18\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/yrue8x18</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/yrue8x18\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnlydWU4eDE4OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/media/graph/graph_0_summary_38b94582.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.741086795926094\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.7096434718370438\n",
            "The running loss is:\n",
            "16.730679839849472\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.6692271935939789\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.939407482743263\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.7975762993097305\n",
            "The running loss is:\n",
            "13.255669929087162\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.5302267971634865\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "11.027616888284683\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.4411046755313873\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.718288138508797\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.26873152554035185\n",
            "The running loss is:\n",
            "7.313029149547219\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.29252116598188876\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['yrue8x18']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.773601979017258\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.5109440791606903\n",
            "The running loss is:\n",
            "6.80379575304687\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.2721518301218748\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.739170776680112\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.18956683106720448\n",
            "2\n",
            "Data saved to: \n",
            "28_May_202006_02PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_02PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  770.853760\n",
            "66  2020-04-22  sub_region  ...             66  769.976440\n",
            "67  2020-04-23  sub_region  ...             67  771.220154\n",
            "68  2020-04-24  sub_region  ...             68  770.911987\n",
            "69  2020-04-25  sub_region  ...             69  770.517700\n",
            "70  2020-04-26  sub_region  ...             70  770.186768\n",
            "71  2020-04-27  sub_region  ...             71  770.193970\n",
            "72  2020-04-28  sub_region  ...             72  770.157593\n",
            "73  2020-04-29  sub_region  ...             73  770.232788\n",
            "74  2020-04-30  sub_region  ...             74  770.960083\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/media/plotly/test_plot_20_57ec849c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: yrue8x18 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180217-yrue8x18/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: yrue8x18\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: s5c7gh6g with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: s5c7gh6g\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/s5c7gh6g\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/s5c7gh6g</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/s5c7gh6g\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnM1YzdnaDZnOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/media/graph/graph_0_summary_25258857.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.64892402663827\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.5055157046903063\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "37.692751033231616\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.3960278160456154\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.47567129507661\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7583581961139485\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.933722829446197\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.7753230677572666\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['s5c7gh6g']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.798208009451628\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.7703040003500603\n",
            "The running loss is:\n",
            "20.184429235756397\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.7475714531761629\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "20.411993622779846\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.755999763806661\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.94804622977972\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.7388165270288786\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_02PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_02PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  474.087341\n",
            "66  2020-04-22  sub_region  ...             66  474.107361\n",
            "67  2020-04-23  sub_region  ...             67  473.755005\n",
            "68  2020-04-24  sub_region  ...             68  474.003113\n",
            "69  2020-04-25  sub_region  ...             69  474.199402\n",
            "70  2020-04-26  sub_region  ...             70  474.221405\n",
            "71  2020-04-27  sub_region  ...             71  474.191833\n",
            "72  2020-04-28  sub_region  ...             72  473.996887\n",
            "73  2020-04-29  sub_region  ...             73  474.185181\n",
            "74  2020-04-30  sub_region  ...             74  473.697998\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/media/plotly/test_plot_16_1f8d7214.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: s5c7gh6g \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/media/plotly/test_plot_all_17_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180233-s5c7gh6g/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: s5c7gh6g\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: zt8tb192 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: zt8tb192\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/zt8tb192\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/zt8tb192</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/zt8tb192\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnp0OHRiMTkyOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180250-zt8tb192/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180250-zt8tb192/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180250-zt8tb192/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180250-zt8tb192/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180250-zt8tb192/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180250-zt8tb192/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180250-zt8tb192/media/graph/graph_0_summary_7ab4a493.graph.json\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180250-zt8tb192/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.37478191871196\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.49536229328562814\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180250-zt8tb192/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180250-zt8tb192/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "37.45775743201375\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.3873243493338425\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180250-zt8tb192/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180250-zt8tb192/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.595209177583456\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7627855250956835\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180250-zt8tb192/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180250-zt8tb192/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.006036780774593\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.7780013622509109\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['zt8tb192']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180250-zt8tb192/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180250-zt8tb192/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.73020259477198\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.7677852812878512\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180250-zt8tb192/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180250-zt8tb192/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.24301341921091\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.7497412377485523\n",
            "1\n",
            "The running loss is:\n",
            "20.167157787829638\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.7469317699196162\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180250-zt8tb192/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180250-zt8tb192/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "2\n",
            "The running loss is:\n",
            "18.895125970244408\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.6998194803794225\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_02PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180250-zt8tb192/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180250-zt8tb192/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_02PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180250-zt8tb192/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  474.395355\n",
            "66  2020-04-22  sub_region  ...             66  474.474243\n",
            "67  2020-04-23  sub_region  ...             67  473.946503\n",
            "68  2020-04-24  sub_region  ...             68  474.251740\n",
            "69  2020-04-25  sub_region  ...             69  474.498688\n",
            "70  2020-04-26  sub_region  ...             70  474.531128\n",
            "71  2020-04-27  sub_region  ...             71  474.499969\n",
            "72  2020-04-28  sub_region  ...             72  474.257050\n",
            "73  2020-04-29  sub_region  ...             73  474.527222\n",
            "74  2020-04-30  sub_region  ...             74  473.818420\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180250-zt8tb192/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180250-zt8tb192/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180250-zt8tb192/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180250-zt8tb192/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180250-zt8tb192/media/plotly/test_plot_16_c257944a.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180250-zt8tb192/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180250-zt8tb192/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: zt8tb192 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180250-zt8tb192/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180250-zt8tb192/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180250-zt8tb192/wandb-summary.json\n",
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            "INFO:wandb.wandb_agent:Cleaning up finished run: zt8tb192\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
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            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
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            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: knbqwf8h\n"
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          "name": "stdout"
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          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/knbqwf8h\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/knbqwf8h</a><br/>\n",
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          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
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        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180312-knbqwf8h/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180312-knbqwf8h/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180312-knbqwf8h/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180312-knbqwf8h/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180312-knbqwf8h/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180312-knbqwf8h/media/graph/graph_0_summary_e54907c4.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180312-knbqwf8h/requirements.txt\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.137208757922053\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.5606373614045205\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180312-knbqwf8h/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180312-knbqwf8h/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "30.88287841156125\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.1438103115393057\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180312-knbqwf8h/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180312-knbqwf8h/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "25.371903955936432\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.9397001465161642\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180312-knbqwf8h/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180312-knbqwf8h/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.891498629003763\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.7737592084816208\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['knbqwf8h']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180312-knbqwf8h/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180312-knbqwf8h/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.58536821603775\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.7624210450384352\n",
            "1\n",
            "The running loss is:\n",
            "21.16692252457142\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.7839600935026452\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180312-knbqwf8h/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180312-knbqwf8h/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "2\n",
            "The running loss is:\n",
            "20.775313809514046\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.7694560670190387\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_03PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180312-knbqwf8h/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180312-knbqwf8h/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_03PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  422.063049\n",
            "66  2020-04-22  sub_region  ...             66  422.065277\n",
            "67  2020-04-23  sub_region  ...             67  422.008148\n",
            "68  2020-04-24  sub_region  ...             68  422.044128\n",
            "69  2020-04-25  sub_region  ...             69  422.075989\n",
            "70  2020-04-26  sub_region  ...             70  422.085114\n",
            "71  2020-04-27  sub_region  ...             71  422.084900\n",
            "72  2020-04-28  sub_region  ...             72  422.078888\n",
            "73  2020-04-29  sub_region  ...             73  422.060028\n",
            "74  2020-04-30  sub_region  ...             74  422.037537\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180312-knbqwf8h/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180312-knbqwf8h/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: knbqwf8h \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180312-knbqwf8h/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180312-knbqwf8h/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180312-knbqwf8h/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180312-knbqwf8h/media/plotly/test_plot_all_15_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180312-knbqwf8h/media/plotly/test_plot_14_7a77c25e.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180312-knbqwf8h/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180312-knbqwf8h/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180312-knbqwf8h/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: knbqwf8h\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: jmxt5fwy with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: jmxt5fwy\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/jmxt5fwy\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/jmxt5fwy</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/jmxt5fwy\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/wandb-metadata.json\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmpteHQ1Znd5OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/media/graph/graph_0_summary_2d11b813.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.071377269923687\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.5581991581453217\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "31.568736284971237\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.1692124549989347\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "25.198651179671288\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.9332833770248625\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['jmxt5fwy']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.102718703448772\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.7815821742018064\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.57193885743618\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.7619236613865252\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.114420972764492\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.7820155915838701\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.067522056400776\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.7802785946815102\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_03PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_03PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  423.483063\n",
            "66  2020-04-22  sub_region  ...             66  423.511169\n",
            "67  2020-04-23  sub_region  ...             67  423.472900\n",
            "68  2020-04-24  sub_region  ...             68  423.498932\n",
            "69  2020-04-25  sub_region  ...             69  423.520294\n",
            "70  2020-04-26  sub_region  ...             70  423.524902\n",
            "71  2020-04-27  sub_region  ...             71  423.520996\n",
            "72  2020-04-28  sub_region  ...             72  423.510651\n",
            "73  2020-04-29  sub_region  ...             73  423.502411\n",
            "74  2020-04-30  sub_region  ...             74  423.487152\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/wandb-summary.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/media/plotly/test_plot_14_0bb7c432.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
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        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: jmxt5fwy \n",
            "\n"
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/media/plotly/test_plot_all_15_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180328-jmxt5fwy/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: jmxt5fwy\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
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          "text": [
            "wandb: Agent Starting Run: wd43ji3h with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: wd43ji3h\n"
          ],
          "name": "stdout"
        },
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          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/wd43ji3h\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/wd43ji3h</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/wd43ji3h\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOndkNDNqaTNoOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
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          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
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        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180346-wd43ji3h/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180346-wd43ji3h/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180346-wd43ji3h/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180346-wd43ji3h/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180346-wd43ji3h/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180346-wd43ji3h/media/graph/graph_0_summary_0d3b0650.graph.json\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "24.238217145204544\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.9322391209694055\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180346-wd43ji3h/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180346-wd43ji3h/wandb-summary.json\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.11776690930128\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.6199141118962032\n",
            "1\n"
          ],
          "name": "stdout"
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          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180346-wd43ji3h/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180346-wd43ji3h/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.947837613523006\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.6518399082124233\n",
            "2\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180346-wd43ji3h/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180346-wd43ji3h/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.64614214003086\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.6786977746165715\n"
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          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['wd43ji3h']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180346-wd43ji3h/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180346-wd43ji3h/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.301386021077633\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.6269763854260628\n",
            "The running loss is:\n",
            "16.042810605838895\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.6170311771476498\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180346-wd43ji3h/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180346-wd43ji3h/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.744363751262426\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.6055524519716318\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180346-wd43ji3h/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180346-wd43ji3h/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.016306111589074\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.6160117735226567\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180346-wd43ji3h/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180346-wd43ji3h/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.813427444547415\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.6082087478672082\n"
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          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180346-wd43ji3h/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180346-wd43ji3h/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.24589281156659\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.6248420312140996\n",
            "Data saved to: \n",
            "28_May_202006_03PM.json\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
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        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_03PM_model.pth\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180346-wd43ji3h/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180346-wd43ji3h/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  509.934052\n",
            "66  2020-04-22  sub_region  ...             66  507.839417\n",
            "67  2020-04-23  sub_region  ...             67  509.408234\n",
            "68  2020-04-24  sub_region  ...             68  509.694031\n",
            "69  2020-04-25  sub_region  ...             69  509.575317\n",
            "70  2020-04-26  sub_region  ...             70  508.830750\n",
            "71  2020-04-27  sub_region  ...             71  509.065674\n",
            "72  2020-04-28  sub_region  ...             72  507.174622\n",
            "73  2020-04-29  sub_region  ...             73  509.588074\n",
            "74  2020-04-30  sub_region  ...             74  507.856110\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180346-wd43ji3h/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180346-wd43ji3h/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: wd43ji3h \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180346-wd43ji3h/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180346-wd43ji3h/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180346-wd43ji3h/media/plotly/test_plot_20_75179c3f.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180346-wd43ji3h/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180346-wd43ji3h/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180346-wd43ji3h/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180346-wd43ji3h/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180346-wd43ji3h/wandb-events.jsonl\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: wd43ji3h\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: bfg54kzp with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: bfg54kzp\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/bfg54kzp\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/bfg54kzp</a><br/>\n",
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        {
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/bfg54kzp\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmJmZzU0a3pwOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/media/graph/graph_0_summary_c8daf13d.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "24.46081606298685\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.9408006178071866\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.266936726868153\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.625651412571852\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.894336327910423\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.6497821664580932\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.256735399365425\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.6637205922832856\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['bfg54kzp']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.231802832335234\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.6243001089359705\n",
            "The running loss is:\n",
            "16.1003286074847\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.6192434079801807\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.810061607509851\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.6080792925965327\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.048187194392085\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.6172379690150802\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.809576485306025\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.6080606340502317\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_04PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "28_May_202006_04PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  529.040771\n",
            "66  2020-04-22  sub_region  ...             66  528.143555\n",
            "67  2020-04-23  sub_region  ...             67  528.838806\n",
            "68  2020-04-24  sub_region  ...             68  528.961365\n",
            "69  2020-04-25  sub_region  ...             69  528.801025\n",
            "70  2020-04-26  sub_region  ...             70  528.515015\n",
            "71  2020-04-27  sub_region  ...             71  528.581177\n",
            "72  2020-04-28  sub_region  ...             72  528.077698\n",
            "73  2020-04-29  sub_region  ...             73  528.772461\n",
            "74  2020-04-30  sub_region  ...             74  528.409302\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/media/plotly/test_plot_18_712b0439.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: bfg54kzp \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/media/plotly/test_plot_all_19_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180408-bfg54kzp/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: bfg54kzp\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 8hcb1kza with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 8hcb1kza\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/8hcb1kza\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/8hcb1kza</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/8hcb1kza\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjhoY2Ixa3phOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180424-8hcb1kza/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180424-8hcb1kza/media/graph/graph_0_summary_963f46d9.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180424-8hcb1kza/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180424-8hcb1kza/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180424-8hcb1kza/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180424-8hcb1kza/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180424-8hcb1kza/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180424-8hcb1kza/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180424-8hcb1kza/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "24.921816915273666\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.958531419818218\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180424-8hcb1kza/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180424-8hcb1kza/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.33100463449955\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.858884793634598\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180424-8hcb1kza/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180424-8hcb1kza/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.428308714181185\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.7857041813146609\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180424-8hcb1kza/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180424-8hcb1kza/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.427796185016632\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.6702998532698705\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['8hcb1kza']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.2829820625484\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.6647300793287846\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180424-8hcb1kza/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180424-8hcb1kza/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "2\n",
            "The running loss is:\n",
            "15.887216325849295\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.6110467817634344\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_04PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180424-8hcb1kza/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180424-8hcb1kza/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_04PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180424-8hcb1kza/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  559.779724\n",
            "66  2020-04-22  sub_region  ...             66  560.009094\n",
            "67  2020-04-23  sub_region  ...             67  560.145813\n",
            "68  2020-04-24  sub_region  ...             68  560.165466\n",
            "69  2020-04-25  sub_region  ...             69  560.001709\n",
            "70  2020-04-26  sub_region  ...             70  560.061584\n",
            "71  2020-04-27  sub_region  ...             71  559.992859\n",
            "72  2020-04-28  sub_region  ...             72  560.228088\n",
            "73  2020-04-29  sub_region  ...             73  559.984314\n",
            "74  2020-04-30  sub_region  ...             74  560.468567\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180424-8hcb1kza/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180424-8hcb1kza/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180424-8hcb1kza/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180424-8hcb1kza/media/plotly/test_plot_12_f72c3f2f.plotly.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180424-8hcb1kza/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180424-8hcb1kza/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 8hcb1kza \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180424-8hcb1kza/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180424-8hcb1kza/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180424-8hcb1kza/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180424-8hcb1kza/media/plotly/test_plot_all_13_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180424-8hcb1kza/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180424-8hcb1kza/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 8hcb1kza\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 4c6avj80 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 4c6avj80\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/4c6avj80\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/4c6avj80</a><br/>\n",
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          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
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          "output_type": "stream",
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
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        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180441-4c6avj80/config.yaml\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180441-4c6avj80/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180441-4c6avj80/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180441-4c6avj80/media/graph/graph_0_summary_4b795b53.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180441-4c6avj80/wandb-metadata.json\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "25.268363758921623\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.9718601445739086\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180441-4c6avj80/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180441-4c6avj80/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.410943418741226\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.8619593622592779\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180441-4c6avj80/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180441-4c6avj80/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.349964793771505\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.7826909536065964\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180441-4c6avj80/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180441-4c6avj80/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.21460761502385\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.6621002928855327\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['4c6avj80']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180441-4c6avj80/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180441-4c6avj80/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.72370159626007\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.6816808306253873\n",
            "2\n",
            "The running loss is:\n",
            "15.540141738951206\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.597697759190431\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180441-4c6avj80/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180441-4c6avj80/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_04PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_04PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180441-4c6avj80/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180441-4c6avj80/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180441-4c6avj80/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  559.067444\n",
            "66  2020-04-22  sub_region  ...             66  559.143799\n",
            "67  2020-04-23  sub_region  ...             67  559.442505\n",
            "68  2020-04-24  sub_region  ...             68  559.442566\n",
            "69  2020-04-25  sub_region  ...             69  559.225464\n",
            "70  2020-04-26  sub_region  ...             70  559.264404\n",
            "71  2020-04-27  sub_region  ...             71  559.198975\n",
            "72  2020-04-28  sub_region  ...             72  559.425171\n",
            "73  2020-04-29  sub_region  ...             73  559.358643\n",
            "74  2020-04-30  sub_region  ...             74  559.806274\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180441-4c6avj80/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180441-4c6avj80/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180441-4c6avj80/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180441-4c6avj80/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180441-4c6avj80/media/plotly/test_plot_12_b8d9f787.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180441-4c6avj80/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180441-4c6avj80/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 4c6avj80 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180441-4c6avj80/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180441-4c6avj80/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180441-4c6avj80/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180441-4c6avj80/media/plotly/test_plot_all_13_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180441-4c6avj80/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180441-4c6avj80/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 4c6avj80\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: dl021jvm with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: dl021jvm\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/dl021jvm\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/dl021jvm</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/dl021jvm\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmRsMDIxanZtOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/media/graph/graph_0_summary_a207cf1d.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.4773468375206\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.819093873500824\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.37137946486473\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.7748551785945892\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.317528434097767\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.6527011373639107\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.260967433452606\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.5704386973381043\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['dl021jvm']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.17140232771635\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.566856093108654\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.054600022733212\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.5621840009093284\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.07607826590538\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.5630431306362152\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.025826334953308\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.5610330533981324\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.500305883586407\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.5400122353434562\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.7346723228693\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.509386892914772\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_05PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_05PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  384.512299\n",
            "66  2020-04-22  sub_region  ...             66  383.916504\n",
            "67  2020-04-23  sub_region  ...             67  384.334045\n",
            "68  2020-04-24  sub_region  ...             68  384.549500\n",
            "69  2020-04-25  sub_region  ...             69  384.431915\n",
            "70  2020-04-26  sub_region  ...             70  384.146332\n",
            "71  2020-04-27  sub_region  ...             71  384.198578\n",
            "72  2020-04-28  sub_region  ...             72  383.738586\n",
            "73  2020-04-29  sub_region  ...             73  384.381348\n",
            "74  2020-04-30  sub_region  ...             74  383.990662\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/wandb-history.jsonl\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/wandb-summary.json\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
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            "wandb: Agent Finished Run: dl021jvm \n",
            "\n"
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/media/plotly\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180458-dl021jvm/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: dl021jvm\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
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          "text": [
            "wandb: Agent Starting Run: vk5kcqsx with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: vk5kcqsx\n"
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          "name": "stdout"
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          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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          "name": "stderr"
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              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/vk5kcqsx\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/vk5kcqsx</a><br/>\n",
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            "DEBUG:wandb.meta:probe for git information\n",
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            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnZrNWtjcXN4OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
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          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
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          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/media/graph/graph_0_summary_f2a463c0.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/wandb-metadata.json\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/media/graph\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.519908547401428\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.8207963418960571\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.348501980304718\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.7739400792121888\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.020245015621185\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.6408098006248474\n",
            "The running loss is:\n",
            "14.293965496122837\n",
            "The number of items in train is: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "25\n",
            "The loss for epoch 3\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0.5717586198449135\n",
            "1\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['vk5kcqsx']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.129456043243408\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.5651782417297363\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.794427633285522\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.5517771053314209\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.02604092657566\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.5210416370630264\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.591085441410542\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.3836434176564217\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.496750168502331\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.29987000674009323\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_05PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_05PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  388.992981\n",
            "66  2020-04-22  sub_region  ...             66  387.531372\n",
            "67  2020-04-23  sub_region  ...             67  388.493744\n",
            "68  2020-04-24  sub_region  ...             68  388.772583\n",
            "69  2020-04-25  sub_region  ...             69  388.547852\n",
            "70  2020-04-26  sub_region  ...             70  387.934692\n",
            "71  2020-04-27  sub_region  ...             71  388.074402\n",
            "72  2020-04-28  sub_region  ...             72  387.295563\n",
            "73  2020-04-29  sub_region  ...             73  388.444427\n",
            "74  2020-04-30  sub_region  ...             74  387.736328\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/media/plotly/test_plot_18_166901d4.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: vk5kcqsx \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/media/plotly/test_plot_all_19_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180519-vk5kcqsx/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: vk5kcqsx\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: k7a8hifp with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: k7a8hifp\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/k7a8hifp\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/k7a8hifp</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/k7a8hifp\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOms3YThoaWZwOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "11.178941723890603\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.4140348786626149\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180538-k7a8hifp/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180538-k7a8hifp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180538-k7a8hifp/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180538-k7a8hifp/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180538-k7a8hifp/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180538-k7a8hifp/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180538-k7a8hifp/media/graph/graph_0_summary_c5a40404.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180538-k7a8hifp/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180538-k7a8hifp/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.482956442050636\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.5364057941500235\n",
            "1\n",
            "The running loss is:\n",
            "12.16446726070717\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.4505358244706359\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180538-k7a8hifp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180538-k7a8hifp/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.8048704912635\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.2890692774542037\n",
            "1\n",
            "The running loss is:\n",
            "7.099390174262226\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.26294037682452687\n",
            "The running loss is:\n",
            "6.51896614592988\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.24144319058999558\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180538-k7a8hifp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180538-k7a8hifp/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.50308578943077\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.24085502923817667\n",
            "1\n",
            "The running loss is:\n",
            "6.709017640678212\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.24848213483993378\n",
            "The running loss is:\n",
            "6.639082251727814\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.2458919352491783\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180538-k7a8hifp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180538-k7a8hifp/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.743373372781207\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.21271753232522989\n",
            "Data saved to: \n",
            "28_May_202006_05PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_05PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['k7a8hifp']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180538-k7a8hifp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180538-k7a8hifp/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180538-k7a8hifp/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  758.606506\n",
            "66  2020-04-22  sub_region  ...             66  838.690002\n",
            "67  2020-04-23  sub_region  ...             67  739.158081\n",
            "68  2020-04-24  sub_region  ...             68  760.182251\n",
            "69  2020-04-25  sub_region  ...             69  791.689392\n",
            "70  2020-04-26  sub_region  ...             70  821.668945\n",
            "71  2020-04-27  sub_region  ...             71  813.191467\n",
            "72  2020-04-28  sub_region  ...             72  824.506714\n",
            "73  2020-04-29  sub_region  ...             73  810.240173\n",
            "74  2020-04-30  sub_region  ...             74  760.039307\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180538-k7a8hifp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180538-k7a8hifp/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: k7a8hifp \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180538-k7a8hifp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180538-k7a8hifp/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180538-k7a8hifp/media/plotly/test_plot_20_50b5ba08.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180538-k7a8hifp/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180538-k7a8hifp/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180538-k7a8hifp/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180538-k7a8hifp/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: k7a8hifp\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 1drnq2sj with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 1drnq2sj\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/1drnq2sj\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/1drnq2sj</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/1drnq2sj\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjFkcm5xMnNqOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "11.243638909421861\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.41643107071932817\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180555-1drnq2sj/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180555-1drnq2sj/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180555-1drnq2sj/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180555-1drnq2sj/media/graph/graph_0_summary_62c56d93.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180555-1drnq2sj/wandb-history.jsonl\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180555-1drnq2sj/requirements.txt\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180555-1drnq2sj/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.065432488219813\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.5579789810451783\n",
            "1\n",
            "The running loss is:\n",
            "9.927668149583042\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.36769141294752006\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180555-1drnq2sj/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180555-1drnq2sj/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.617937269620597\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.2821458248007629\n",
            "1\n",
            "The running loss is:\n",
            "6.723564739339054\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.2490209162718168\n",
            "The running loss is:\n",
            "7.1353028768789954\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.2642704769214443\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180555-1drnq2sj/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180555-1drnq2sj/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.726659771054983\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.21209851003907346\n",
            "2\n",
            "The running loss is:\n",
            "6.842962785856798\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.2534430661428444\n",
            "The running loss is:\n",
            "6.147264284198172\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.22767645497030267\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180555-1drnq2sj/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180555-1drnq2sj/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.772955868189456\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.25085021734035023\n",
            "Data saved to: \n",
            "28_May_202006_05PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_05PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['1drnq2sj']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180555-1drnq2sj/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180555-1drnq2sj/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180555-1drnq2sj/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  702.101074\n",
            "66  2020-04-22  sub_region  ...             66  799.573730\n",
            "67  2020-04-23  sub_region  ...             67  688.857788\n",
            "68  2020-04-24  sub_region  ...             68  695.529663\n",
            "69  2020-04-25  sub_region  ...             69  724.923462\n",
            "70  2020-04-26  sub_region  ...             70  761.772095\n",
            "71  2020-04-27  sub_region  ...             71  748.695679\n",
            "72  2020-04-28  sub_region  ...             72  803.100220\n",
            "73  2020-04-29  sub_region  ...             73  733.273804\n",
            "74  2020-04-30  sub_region  ...             74  718.350464\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180555-1drnq2sj/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180555-1drnq2sj/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180555-1drnq2sj/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180555-1drnq2sj/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180555-1drnq2sj/media/plotly/test_plot_20_e049cd21.plotly.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180555-1drnq2sj/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180555-1drnq2sj/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 1drnq2sj \n",
            "\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180555-1drnq2sj/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180555-1drnq2sj/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180555-1drnq2sj/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180555-1drnq2sj/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180555-1drnq2sj/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180555-1drnq2sj/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 1drnq2sj\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: rrenm2co with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: rrenm2co\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/rrenm2co\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/rrenm2co</a><br/>\n",
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            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/rrenm2co\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
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            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
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          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "13.754779417067766\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.5094362747062136\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180611-rrenm2co/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180611-rrenm2co/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180611-rrenm2co/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180611-rrenm2co/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180611-rrenm2co/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180611-rrenm2co/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180611-rrenm2co/media/graph/graph_0_summary_73f15b66.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180611-rrenm2co/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180611-rrenm2co/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.335549898445606\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.5679833295720594\n",
            "1\n",
            "The running loss is:\n",
            "12.792411483824253\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.4737930179194168\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180611-rrenm2co/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180611-rrenm2co/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.463905839249492\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.38755206812035153\n",
            "1\n",
            "The running loss is:\n",
            "8.178016487509012\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.3028894995373708\n",
            "The running loss is:\n",
            "7.196833549998701\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.2665493907406926\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180611-rrenm2co/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180611-rrenm2co/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.724783644080162\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.2861030979288949\n",
            "2\n",
            "The running loss is:\n",
            "7.328360717743635\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.27142076732383835\n",
            "The running loss is:\n",
            "8.112628399394453\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.30046771849609083\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180611-rrenm2co/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180611-rrenm2co/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.612887730821967\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.244921767808221\n",
            "Data saved to: \n",
            "28_May_202006_06PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_06PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['rrenm2co']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  785.347290\n",
            "66  2020-04-22  sub_region  ...             66  848.155396\n",
            "67  2020-04-23  sub_region  ...             67  778.021973\n",
            "68  2020-04-24  sub_region  ...             68  802.694946\n",
            "69  2020-04-25  sub_region  ...             69  814.782837\n",
            "70  2020-04-26  sub_region  ...             70  827.907227\n",
            "71  2020-04-27  sub_region  ...             71  817.994751\n",
            "72  2020-04-28  sub_region  ...             72  834.097107\n",
            "73  2020-04-29  sub_region  ...             73  812.438049\n",
            "74  2020-04-30  sub_region  ...             74  797.486328\n",
            "\n",
            "[21 rows x 28 columns]"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180611-rrenm2co/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180611-rrenm2co/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180611-rrenm2co/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180611-rrenm2co/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180611-rrenm2co/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180611-rrenm2co/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180611-rrenm2co/media/plotly/test_plot_20_e9ff783e.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180611-rrenm2co/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180611-rrenm2co/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: rrenm2co \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180611-rrenm2co/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180611-rrenm2co/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180611-rrenm2co/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180611-rrenm2co/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180611-rrenm2co/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180611-rrenm2co/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: rrenm2co\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 0ivbk110 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 0ivbk110\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/0ivbk110\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/0ivbk110</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/0ivbk110\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjBpdmJrMTEwOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "13.334896691143513\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.4938850626349449\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180626-0ivbk110/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180626-0ivbk110/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180626-0ivbk110/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180626-0ivbk110/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180626-0ivbk110/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180626-0ivbk110/media/graph/graph_0_summary_4900a83a.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180626-0ivbk110/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180626-0ivbk110/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180626-0ivbk110/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.006994970142841\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.5558146285238089\n",
            "1\n",
            "The running loss is:\n",
            "11.964153863489628\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.44311680975887513\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180626-0ivbk110/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180626-0ivbk110/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.46366179548204\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.3505059924252607\n",
            "1\n",
            "The running loss is:\n",
            "7.935978587716818\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.29392513287840066\n",
            "The running loss is:\n",
            "7.160205790773034\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.26519280706566795\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180626-0ivbk110/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180626-0ivbk110/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.432532018050551\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.2752789636315019\n",
            "2\n",
            "The running loss is:\n",
            "6.930170862004161\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.256672994889043\n",
            "The running loss is:\n",
            "7.583688434679061\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.2808773494325578\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180626-0ivbk110/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180626-0ivbk110/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.852206656709313\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.2537854317299746\n",
            "Data saved to: \n",
            "28_May_202006_06PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_06PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['0ivbk110']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180626-0ivbk110/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180626-0ivbk110/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180626-0ivbk110/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  792.838745\n",
            "66  2020-04-22  sub_region  ...             66  839.469238\n",
            "67  2020-04-23  sub_region  ...             67  793.361755\n",
            "68  2020-04-24  sub_region  ...             68  806.429504\n",
            "69  2020-04-25  sub_region  ...             69  816.609619\n",
            "70  2020-04-26  sub_region  ...             70  828.707764\n",
            "71  2020-04-27  sub_region  ...             71  823.823608\n",
            "72  2020-04-28  sub_region  ...             72  829.279419\n",
            "73  2020-04-29  sub_region  ...             73  820.153503\n",
            "74  2020-04-30  sub_region  ...             74  806.989746\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180626-0ivbk110/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180626-0ivbk110/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180626-0ivbk110/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180626-0ivbk110/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180626-0ivbk110/media/plotly/test_plot_20_4bc95361.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180626-0ivbk110/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180626-0ivbk110/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 0ivbk110 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180626-0ivbk110/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180626-0ivbk110/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180626-0ivbk110/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180626-0ivbk110/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180626-0ivbk110/media/plotly\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180626-0ivbk110/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 0ivbk110\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: djyhq8by with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: djyhq8by\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/djyhq8by\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/djyhq8by</a><br/>\n",
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            "DEBUG:wandb.meta:probe for git information\n",
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            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
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          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "18.989742126315832\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.7303746971659936\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180638-djyhq8by/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180638-djyhq8by/wandb-history.jsonl\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180638-djyhq8by/requirements.txt\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.38419210910797\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.6301612349656912\n",
            "1\n",
            "The running loss is:\n",
            "15.279926925897598\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.5876894971499076\n",
            "The running loss is:\n",
            "10.945733822882175\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.4209897624185452\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180638-djyhq8by/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180638-djyhq8by/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "5.606289468705654\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.21562651802714056\n",
            "2\n",
            "The running loss is:\n",
            "4.256948810070753\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.16372880038733667\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180638-djyhq8by/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180638-djyhq8by/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.631639575585723\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.17813998367637396\n",
            "1\n",
            "The running loss is:\n",
            "6.9152334509417415\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.26597051734391314\n",
            "The running loss is:\n",
            "3.967709585092962\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.15260421481126776\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180638-djyhq8by/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180638-djyhq8by/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "3.5715523255057633\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.13736739713483706\n",
            "Data saved to: \n",
            "28_May_202006_06PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_06PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['djyhq8by']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180638-djyhq8by/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180638-djyhq8by/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180638-djyhq8by/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  814.696899\n",
            "66  2020-04-22  sub_region  ...             66  825.249207\n",
            "67  2020-04-23  sub_region  ...             67  843.944824\n",
            "68  2020-04-24  sub_region  ...             68  816.804077\n",
            "69  2020-04-25  sub_region  ...             69  796.358887\n",
            "70  2020-04-26  sub_region  ...             70  799.779358\n",
            "71  2020-04-27  sub_region  ...             71  801.697021\n",
            "72  2020-04-28  sub_region  ...             72  800.930298\n",
            "73  2020-04-29  sub_region  ...             73  815.445740\n",
            "74  2020-04-30  sub_region  ...             74  822.305115\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180638-djyhq8by/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180638-djyhq8by/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180638-djyhq8by/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180638-djyhq8by/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180638-djyhq8by/media/plotly/test_plot_20_d82f34c9.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180638-djyhq8by/media\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180638-djyhq8by/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: djyhq8by \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180638-djyhq8by/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180638-djyhq8by/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180638-djyhq8by/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180638-djyhq8by/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180638-djyhq8by/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180638-djyhq8by/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: djyhq8by\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 0a8lnzah with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 0a8lnzah\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/0a8lnzah\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/0a8lnzah</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/0a8lnzah\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjBhOGxuemFoOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "18.905425552278757\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.7271317520107214\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180655-0a8lnzah/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180655-0a8lnzah/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180655-0a8lnzah/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180655-0a8lnzah/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180655-0a8lnzah/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180655-0a8lnzah/media/graph/graph_0_summary_7ac0ae2a.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180655-0a8lnzah/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180655-0a8lnzah/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180655-0a8lnzah/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.324342384934425\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.6278593224974779\n",
            "1\n",
            "The running loss is:\n",
            "15.281524695456028\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.5877509498252318\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180655-0a8lnzah/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180655-0a8lnzah/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.686566263437271\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.41102177936297196\n",
            "1\n",
            "The running loss is:\n",
            "6.059355471283197\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.2330521335108922\n",
            "The running loss is:\n",
            "4.697019984945655\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.1806546148056021\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180655-0a8lnzah/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180655-0a8lnzah/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.977002476342022\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.26834624909007776\n",
            "1\n",
            "The running loss is:\n",
            "5.771942357998341\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.2219977829999362\n",
            "The running loss is:\n",
            "4.5645317947492\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.17555891518266156\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180655-0a8lnzah/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180655-0a8lnzah/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.686972144991159\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.21872969788427538\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202006_06PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_06PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['0a8lnzah']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180655-0a8lnzah/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180655-0a8lnzah/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180655-0a8lnzah/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  736.851746\n",
            "66  2020-04-22  sub_region  ...             66  772.893066\n",
            "67  2020-04-23  sub_region  ...             67  762.027588\n",
            "68  2020-04-24  sub_region  ...             68  752.332520\n",
            "69  2020-04-25  sub_region  ...             69  736.720947\n",
            "70  2020-04-26  sub_region  ...             70  741.464417\n",
            "71  2020-04-27  sub_region  ...             71  733.462769\n",
            "72  2020-04-28  sub_region  ...             72  760.639526\n",
            "73  2020-04-29  sub_region  ...             73  746.799805\n",
            "74  2020-04-30  sub_region  ...             74  770.807861\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180655-0a8lnzah/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180655-0a8lnzah/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180655-0a8lnzah/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180655-0a8lnzah/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180655-0a8lnzah/media/plotly/test_plot_20_556d85cf.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180655-0a8lnzah/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180655-0a8lnzah/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 0a8lnzah \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180655-0a8lnzah/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180655-0a8lnzah/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180655-0a8lnzah/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180655-0a8lnzah/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180655-0a8lnzah/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180655-0a8lnzah/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 0a8lnzah\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
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            "wandb: Agent Starting Run: 86pghp1t with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 86pghp1t\n"
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          "name": "stdout"
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          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/86pghp1t\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/86pghp1t</a><br/>\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/86pghp1t\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
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            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjg2cGdocDF0OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
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          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "16.948153391480446\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.6518520535184786\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180711-86pghp1t/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180711-86pghp1t/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180711-86pghp1t/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180711-86pghp1t/media/graph/graph_0_summary_30e7d961.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180711-86pghp1t/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180711-86pghp1t/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180711-86pghp1t/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180711-86pghp1t/media\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.85379159078002\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.5328381381069238\n",
            "The running loss is:\n",
            "9.64835274219513\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.37109049008442807\n",
            "1\n",
            "The running loss is:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180711-86pghp1t/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "8.4732926171273\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180711-86pghp1t/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.3258958698895115\n",
            "The running loss is:\n",
            "5.7244476936757565\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.22017106514137524\n",
            "The running loss is:\n",
            "6.5513798370957375\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.25197614758060527\n",
            "1\n",
            "The running loss is:\n",
            "8.37942417897284\n",
            "The number of items in train is: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180711-86pghp1t/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "26\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180711-86pghp1t/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The loss for epoch 6\n",
            "0.3222855453451092\n",
            "The running loss is:\n",
            "4.463485406711698\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.1716725156427576\n",
            "1\n",
            "The running loss is:\n",
            "4.722438246943057\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.18163224026704064\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180711-86pghp1t/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180711-86pghp1t/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.340588949620724\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.1669457288315663\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202006_07PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_07PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['86pghp1t']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  851.006592\n",
            "66  2020-04-22  sub_region  ...             66  861.678711\n",
            "67  2020-04-23  sub_region  ...             67  870.574524\n",
            "68  2020-04-24  sub_region  ...             68  854.424133\n",
            "69  2020-04-25  sub_region  ...             69  835.877502\n",
            "70  2020-04-26  sub_region  ...             70  840.583374\n",
            "71  2020-04-27  sub_region  ...             71  839.167969\n",
            "72  2020-04-28  sub_region  ...             72  850.335999\n",
            "73  2020-04-29  sub_region  ...             73  848.938354\n",
            "74  2020-04-30  sub_region  ...             74  859.935547\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180711-86pghp1t/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180711-86pghp1t/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180711-86pghp1t/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180711-86pghp1t/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180711-86pghp1t/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180711-86pghp1t/media/plotly/test_plot_20_0f4d1d99.plotly.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 86pghp1t \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180711-86pghp1t/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180711-86pghp1t/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180711-86pghp1t/media/plotly\n",
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180711-86pghp1t/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180711-86pghp1t/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180711-86pghp1t/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 86pghp1t\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: xl3jrbac with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: xl3jrbac\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/xl3jrbac\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/xl3jrbac</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/xl3jrbac\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnhsM2pyYmFjOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "16.634709760546684\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.6397965292517955\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180725-xl3jrbac/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180725-xl3jrbac/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180725-xl3jrbac/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180725-xl3jrbac/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180725-xl3jrbac/media/graph/graph_0_summary_cf6f021e.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180725-xl3jrbac/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180725-xl3jrbac/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180725-xl3jrbac/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180725-xl3jrbac/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.594469293951988\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.5613257420750765\n",
            "The running loss is:\n",
            "9.017393708229065\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.3468228349318871\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180725-xl3jrbac/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180725-xl3jrbac/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.2338755913078785\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.27822598428107226\n",
            "The running loss is:\n",
            "4.978128891438246\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.19146649582454792\n",
            "1\n",
            "The running loss is:\n",
            "5.498803533613682\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.21149244360052621\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180725-xl3jrbac/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180725-xl3jrbac/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.14033218100667\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.27462816080794883\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_07PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_07PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180725-xl3jrbac/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180725-xl3jrbac/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180725-xl3jrbac/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  736.526611\n",
            "66  2020-04-22  sub_region  ...             66  758.064575\n",
            "67  2020-04-23  sub_region  ...             67  751.773193\n",
            "68  2020-04-24  sub_region  ...             68  746.766174\n",
            "69  2020-04-25  sub_region  ...             69  738.920166\n",
            "70  2020-04-26  sub_region  ...             70  743.997314\n",
            "71  2020-04-27  sub_region  ...             71  741.321045\n",
            "72  2020-04-28  sub_region  ...             72  750.091064\n",
            "73  2020-04-29  sub_region  ...             73  743.901367\n",
            "74  2020-04-30  sub_region  ...             74  754.618958\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['xl3jrbac']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180725-xl3jrbac/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180725-xl3jrbac/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180725-xl3jrbac/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180725-xl3jrbac/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180725-xl3jrbac/media/plotly/test_plot_14_251cab71.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180725-xl3jrbac/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180725-xl3jrbac/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: xl3jrbac \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180725-xl3jrbac/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180725-xl3jrbac/wandb-metadata.json\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180725-xl3jrbac/wandb-events.jsonl\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180725-xl3jrbac/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: xl3jrbac\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
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            "wandb: Agent Starting Run: 19jeisii with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 19jeisii\n"
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          "name": "stdout"
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          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/19jeisii\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/19jeisii</a><br/>\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/19jeisii\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
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          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "14.528097435832024\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.581123897433281\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180737-19jeisii/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180737-19jeisii/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180737-19jeisii/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180737-19jeisii/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180737-19jeisii/wandb-events.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.321749061346054\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180737-19jeisii/media/graph/graph_0_summary_d94cc4a3.graph.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0.5728699624538421\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180737-19jeisii/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180737-19jeisii/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180737-19jeisii/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.53690830618143\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.3414763322472572\n",
            "1\n",
            "The running loss is:\n",
            "3.877267986536026\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.15509071946144104\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180737-19jeisii/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180737-19jeisii/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "3.9804319692775607\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.15921727877110242\n",
            "1\n",
            "The running loss is:\n",
            "4.088011069223285\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.16352044276893138\n",
            "The running loss is:\n",
            "3.642200954724103\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.14568803818896414\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180737-19jeisii/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180737-19jeisii/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.692892727442086\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.18771570909768343\n",
            "2\n",
            "The running loss is:\n",
            "3.2474756240844727\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.1298990249633789\n",
            "The running loss is:\n",
            "3.19531544810161\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.1278126179240644\n",
            "Data saved to: \n",
            "28_May_202006_07PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180737-19jeisii/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180737-19jeisii/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_07PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['19jeisii']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  711.413086\n",
            "66  2020-04-22  sub_region  ...             66  714.489502\n",
            "67  2020-04-23  sub_region  ...             67  749.420166\n",
            "68  2020-04-24  sub_region  ...             68  730.427063\n",
            "69  2020-04-25  sub_region  ...             69  707.012146\n",
            "70  2020-04-26  sub_region  ...             70  700.846252\n",
            "71  2020-04-27  sub_region  ...             71  701.634644\n",
            "72  2020-04-28  sub_region  ...             72  702.439697\n",
            "73  2020-04-29  sub_region  ...             73  710.740356\n",
            "74  2020-04-30  sub_region  ...             74  736.382812\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180737-19jeisii/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180737-19jeisii/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180737-19jeisii/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180737-19jeisii/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180737-19jeisii/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180737-19jeisii/media/plotly/test_plot_20_b4b65440.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180737-19jeisii/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180737-19jeisii/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 19jeisii \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180737-19jeisii/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180737-19jeisii/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180737-19jeisii/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180737-19jeisii/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180737-19jeisii/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180737-19jeisii/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 19jeisii\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: mdyyzx0m with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: mdyyzx0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/mdyyzx0m\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/mdyyzx0m</a><br/>\n",
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/mdyyzx0m\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOm1keXl6eDBtOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "14.583468616008759\n",
            "The number of items in train is: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180751-mdyyzx0m/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "25\n",
            "The loss for epoch 0\n",
            "0.5833387446403503\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180751-mdyyzx0m/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180751-mdyyzx0m/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180751-mdyyzx0m/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180751-mdyyzx0m/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180751-mdyyzx0m/media/graph/graph_0_summary_67273979.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180751-mdyyzx0m/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180751-mdyyzx0m/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180751-mdyyzx0m/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.234198205173016\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.4493679282069206\n",
            "1\n",
            "The running loss is:\n",
            "6.8342317044734955\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.2733692681789398\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180751-mdyyzx0m/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180751-mdyyzx0m/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.914771635085344\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.19659086540341378\n",
            "The running loss is:\n",
            "6.097113943658769\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.24388455774635076\n",
            "1\n",
            "The running loss is:\n",
            "5.855831284075975\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.23423325136303902\n",
            "2\n",
            "The running loss is:\n",
            "4.823610462248325\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.19294441848993302\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180751-mdyyzx0m/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180751-mdyyzx0m/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.116255223751068\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.2046502089500427\n",
            "1\n",
            "The running loss is:\n",
            "3.9354080138728023\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.1574163205549121\n",
            "2\n",
            "The running loss is:\n",
            "3.635712749324739\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.14542850997298956\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180751-mdyyzx0m/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180751-mdyyzx0m/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_07PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_07PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['mdyyzx0m']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180751-mdyyzx0m/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180751-mdyyzx0m/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180751-mdyyzx0m/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  742.793396\n",
            "66  2020-04-22  sub_region  ...             66  736.652344\n",
            "67  2020-04-23  sub_region  ...             67  754.830688\n",
            "68  2020-04-24  sub_region  ...             68  749.173950\n",
            "69  2020-04-25  sub_region  ...             69  738.829346\n",
            "70  2020-04-26  sub_region  ...             70  734.617004\n",
            "71  2020-04-27  sub_region  ...             71  732.794556\n",
            "72  2020-04-28  sub_region  ...             72  736.974976\n",
            "73  2020-04-29  sub_region  ...             73  737.531616\n",
            "74  2020-04-30  sub_region  ...             74  753.737549\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180751-mdyyzx0m/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180751-mdyyzx0m/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180751-mdyyzx0m/wandb-history.jsonl\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180751-mdyyzx0m/media/plotly/test_plot_20_71421788.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180751-mdyyzx0m/media\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180751-mdyyzx0m/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
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          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: mdyyzx0m \n",
            "\n"
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180751-mdyyzx0m/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180751-mdyyzx0m/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180751-mdyyzx0m/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180751-mdyyzx0m/media/plotly\n",
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            "INFO:wandb.wandb_agent:Cleaning up finished run: mdyyzx0m\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
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          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: qyfcznp8 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: qyfcznp8\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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          "name": "stderr"
        },
        {
          "output_type": "display_data",
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/qyfcznp8\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/qyfcznp8</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/qyfcznp8\n",
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            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
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        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180802-qyfcznp8/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180802-qyfcznp8/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180802-qyfcznp8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180802-qyfcznp8/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180802-qyfcznp8/media/graph/graph_0_summary_7c646643.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180802-qyfcznp8/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180802-qyfcznp8/wandb-events.jsonl\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180802-qyfcznp8/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.349453534625354\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.4573871679490872\n",
            "The running loss is:\n",
            "30.535183822968975\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.130932734184036\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180802-qyfcznp8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180802-qyfcznp8/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.304419979453087\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.6779414807204847\n",
            "1\n",
            "The running loss is:\n",
            "10.870923589682207\n",
            "The number of items in train is: \n",
            "27\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180802-qyfcznp8/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The loss for epoch 3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180802-qyfcznp8/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0.4026267996178595\n",
            "2\n",
            "The running loss is:\n",
            "9.898105231113732\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.36659649004124933\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_08PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_08PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180802-qyfcznp8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180802-qyfcznp8/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180802-qyfcznp8/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  502.529541\n",
            "66  2020-04-22  sub_region  ...             66  492.594849\n",
            "67  2020-04-23  sub_region  ...             67  496.524628\n",
            "68  2020-04-24  sub_region  ...             68  495.569824\n",
            "69  2020-04-25  sub_region  ...             69  495.434570\n",
            "70  2020-04-26  sub_region  ...             70  493.173859\n",
            "71  2020-04-27  sub_region  ...             71  493.890808\n",
            "72  2020-04-28  sub_region  ...             72  491.183136\n",
            "73  2020-04-29  sub_region  ...             73  497.434570\n",
            "74  2020-04-30  sub_region  ...             74  489.901245\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['qyfcznp8']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180802-qyfcznp8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180802-qyfcznp8/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180802-qyfcznp8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180802-qyfcznp8/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180802-qyfcznp8/media/plotly/test_plot_10_c557f1bd.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180802-qyfcznp8/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180802-qyfcznp8/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: qyfcznp8 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180802-qyfcznp8/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180802-qyfcznp8/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180802-qyfcznp8/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180802-qyfcznp8/media/plotly/test_plot_all_11_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180802-qyfcznp8/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180802-qyfcznp8/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: qyfcznp8\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: d6rezp1v with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: d6rezp1v\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/d6rezp1v\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/d6rezp1v</a><br/>\n",
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          "metadata": {
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        {
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/d6rezp1v\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmQ2cmV6cDF2OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180814-d6rezp1v/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180814-d6rezp1v/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180814-d6rezp1v/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180814-d6rezp1v/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180814-d6rezp1v/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180814-d6rezp1v/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180814-d6rezp1v/media/graph/graph_0_summary_902a2889.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180814-d6rezp1v/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180814-d6rezp1v/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.00125639885664\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.4444909777354311\n",
            "The running loss is:\n",
            "27.564760353183374\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.0209170501179028\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180814-d6rezp1v/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180814-d6rezp1v/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.1493139838567\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7092338512539519\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180814-d6rezp1v/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180814-d6rezp1v/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.482985021779314\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.3512216674733079\n",
            "The running loss is:\n",
            "8.379846808500588\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.31036469661113286\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180814-d6rezp1v/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180814-d6rezp1v/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.564052293615532\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.24311304791168636\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['d6rezp1v']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.043116731874761\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.2608561752546208\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180814-d6rezp1v/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180814-d6rezp1v/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.5104550529504195\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.2411279649240896\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180814-d6rezp1v/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180814-d6rezp1v/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.045392430853099\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.2609404604019666\n",
            "The running loss is:\n",
            "6.926817309111357\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.25654878922634655\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202006_08PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180814-d6rezp1v/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180814-d6rezp1v/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_08PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  778.043945\n",
            "66  2020-04-22  sub_region  ...             66  783.455933\n",
            "67  2020-04-23  sub_region  ...             67  766.487671\n",
            "68  2020-04-24  sub_region  ...             68  773.625916\n",
            "69  2020-04-25  sub_region  ...             69  782.676147\n",
            "70  2020-04-26  sub_region  ...             70  784.909546\n",
            "71  2020-04-27  sub_region  ...             71  784.748901\n",
            "72  2020-04-28  sub_region  ...             72  778.614258\n",
            "73  2020-04-29  sub_region  ...             73  782.117676\n",
            "74  2020-04-30  sub_region  ...             74  761.960938\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180814-d6rezp1v/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180814-d6rezp1v/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180814-d6rezp1v/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180814-d6rezp1v/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180814-d6rezp1v/media/plotly/test_plot_20_647b6c30.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180814-d6rezp1v/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180814-d6rezp1v/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: d6rezp1v \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180814-d6rezp1v/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180814-d6rezp1v/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180814-d6rezp1v/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180814-d6rezp1v/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180814-d6rezp1v/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180814-d6rezp1v/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: d6rezp1v\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: uidcomdz with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: uidcomdz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/uidcomdz\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/uidcomdz</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/uidcomdz\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnVpZGNvbWR6OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180831-uidcomdz/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180831-uidcomdz/media/graph/graph_0_summary_9d362d29.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180831-uidcomdz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180831-uidcomdz/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180831-uidcomdz/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180831-uidcomdz/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180831-uidcomdz/wandb-events.jsonl\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180831-uidcomdz/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.362042212858796\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.5689645264021777\n",
            "The running loss is:\n",
            "18.604257106781006\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.6890465595104076\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180831-uidcomdz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180831-uidcomdz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.387704892084002\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7180631441512594\n",
            "The running loss is:\n",
            "17.115502156317234\n",
            "The number of items in train is: \n",
            "27\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180831-uidcomdz/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The loss for epoch 3\n",
            "0.6339074872710087\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180831-uidcomdz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.399747041985393\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.3481387793327923\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180831-uidcomdz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180831-uidcomdz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.062234118580818\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.2986012636511414\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['uidcomdz']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.508644045330584\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.2780979276048364\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180831-uidcomdz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180831-uidcomdz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.624856340698898\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.2824020866925518\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180831-uidcomdz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180831-uidcomdz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.4580579325556755\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.27622436787243243\n",
            "The running loss is:\n",
            "6.958448266610503\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.25772030617075936\n",
            "Data saved to: \n",
            "28_May_202006_08PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180831-uidcomdz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180831-uidcomdz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_08PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  772.555298\n",
            "66  2020-04-22  sub_region  ...             66  766.406433\n",
            "67  2020-04-23  sub_region  ...             67  773.479492\n",
            "68  2020-04-24  sub_region  ...             68  770.044922\n",
            "69  2020-04-25  sub_region  ...             69  772.980347\n",
            "70  2020-04-26  sub_region  ...             70  772.595215\n",
            "71  2020-04-27  sub_region  ...             71  772.935425\n",
            "72  2020-04-28  sub_region  ...             72  770.757202\n",
            "73  2020-04-29  sub_region  ...             73  772.154602\n",
            "74  2020-04-30  sub_region  ...             74  771.334900\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180831-uidcomdz/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180831-uidcomdz/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: uidcomdz \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180831-uidcomdz/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180831-uidcomdz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180831-uidcomdz/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180831-uidcomdz/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180831-uidcomdz/media/plotly/test_plot_20_cf3ed598.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180831-uidcomdz/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180831-uidcomdz/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180831-uidcomdz/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: uidcomdz\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: gezep2gw with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: gezep2gw\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/gezep2gw\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/gezep2gw</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/gezep2gw\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmdlemVwMmd3OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180848-gezep2gw/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180848-gezep2gw/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180848-gezep2gw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180848-gezep2gw/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180848-gezep2gw/media/graph/graph_0_summary_69bc7e29.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180848-gezep2gw/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180848-gezep2gw/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180848-gezep2gw/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180848-gezep2gw/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.227613762952387\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.5639856949241625\n",
            "The running loss is:\n",
            "17.898133425042033\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "0.6628938305571124\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180848-gezep2gw/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180848-gezep2gw/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.654380347579718\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.5797918647251747\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180848-gezep2gw/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180848-gezep2gw/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.89348728954792\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.3664254551684415\n",
            "1\n",
            "The running loss is:\n",
            "8.431944162817672\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.31229422825250636\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180848-gezep2gw/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180848-gezep2gw/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.609166414942592\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.31885801536824415\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['gezep2gw']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.016866168007255\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.3339580062224909\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180848-gezep2gw/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180848-gezep2gw/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.0558643322438\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.29836534563865924\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180848-gezep2gw/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180848-gezep2gw/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.685117572546005\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 8\n",
            "0.3216710212054076\n",
            "1\n",
            "The running loss is:\n",
            "6.745234100148082\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 9\n",
            "0.2498234851906697\n",
            "Data saved to: \n",
            "28_May_202006_08PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180848-gezep2gw/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180848-gezep2gw/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_08PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  783.742432\n",
            "66  2020-04-22  sub_region  ...             66  782.695801\n",
            "67  2020-04-23  sub_region  ...             67  783.955322\n",
            "68  2020-04-24  sub_region  ...             68  783.325439\n",
            "69  2020-04-25  sub_region  ...             69  783.187744\n",
            "70  2020-04-26  sub_region  ...             70  783.111938\n",
            "71  2020-04-27  sub_region  ...             71  783.345886\n",
            "72  2020-04-28  sub_region  ...             72  782.962280\n",
            "73  2020-04-29  sub_region  ...             73  782.751831\n",
            "74  2020-04-30  sub_region  ...             74  783.127808\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180848-gezep2gw/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180848-gezep2gw/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180848-gezep2gw/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180848-gezep2gw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180848-gezep2gw/media/plotly/test_plot_20_86f33ef2.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180848-gezep2gw/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180848-gezep2gw/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: gezep2gw \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180848-gezep2gw/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180848-gezep2gw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180848-gezep2gw/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180848-gezep2gw/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180848-gezep2gw/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180848-gezep2gw/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: gezep2gw\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 3xj34yik with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 3xj34yik\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/3xj34yik\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/3xj34yik</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/3xj34yik\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjN4ajM0eWlrOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180905-3xj34yik/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180905-3xj34yik/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180905-3xj34yik/media/graph/graph_0_summary_0da95377.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180905-3xj34yik/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180905-3xj34yik/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180905-3xj34yik/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180905-3xj34yik/wandb-history.jsonl\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180905-3xj34yik/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.012039236724377\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.8081553552586299\n",
            "The running loss is:\n",
            "16.833910040557384\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.6474580784829763\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180905-3xj34yik/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180905-3xj34yik/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.23337048664689\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.6243604033325727\n",
            "The running loss is:\n",
            "15.538650223985314\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.5976403932302043\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180905-3xj34yik/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180905-3xj34yik/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "15.044064596295357\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.5786178690882829\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180905-3xj34yik/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180905-3xj34yik/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.831363804638386\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.4550524540245533\n",
            "The running loss is:\n",
            "7.703582746908069\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.2962916441118488\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['3xj34yik']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180905-3xj34yik/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180905-3xj34yik/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.853043738752604\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.3405016822597155\n",
            "2\n",
            "The running loss is:\n",
            "5.49346605874598\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.21128715610561463\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180905-3xj34yik/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180905-3xj34yik/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.049623145721853\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.23267781329699433\n",
            "Data saved to: \n",
            "28_May_202006_09PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_09PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180905-3xj34yik/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180905-3xj34yik/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180905-3xj34yik/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  793.348389\n",
            "66  2020-04-22  sub_region  ...             66  793.042908\n",
            "67  2020-04-23  sub_region  ...             67  790.546631\n",
            "68  2020-04-24  sub_region  ...             68  791.214233\n",
            "69  2020-04-25  sub_region  ...             69  792.006836\n",
            "70  2020-04-26  sub_region  ...             70  792.608032\n",
            "71  2020-04-27  sub_region  ...             71  792.963989\n",
            "72  2020-04-28  sub_region  ...             72  789.634521\n",
            "73  2020-04-29  sub_region  ...             73  793.287476\n",
            "74  2020-04-30  sub_region  ...             74  785.882202\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180905-3xj34yik/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180905-3xj34yik/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180905-3xj34yik/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180905-3xj34yik/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180905-3xj34yik/media/plotly/test_plot_20_ef160644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180905-3xj34yik/media\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180905-3xj34yik/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 3xj34yik \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180905-3xj34yik/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180905-3xj34yik/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180905-3xj34yik/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180905-3xj34yik/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180905-3xj34yik/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180905-3xj34yik/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 3xj34yik\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: m7imp1fv with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: m7imp1fv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/m7imp1fv\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/m7imp1fv</a><br/>\n",
              "            "
            ],
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/m7imp1fv\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOm03aW1wMWZ2OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180922-m7imp1fv/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180922-m7imp1fv/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180922-m7imp1fv/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180922-m7imp1fv/requirements.txt\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.714700505137444\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180922-m7imp1fv/wandb-events.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The number of items in train is: \n",
            "26\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180922-m7imp1fv/media/graph/graph_0_summary_1c6cc7d6.graph.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The loss for epoch 0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180922-m7imp1fv/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0.7967192501975939\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180922-m7imp1fv/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180922-m7imp1fv/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.891354955732822\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.6496674982974162\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180922-m7imp1fv/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180922-m7imp1fv/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.139072712510824\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.620733565865801\n",
            "1\n",
            "The running loss is:\n",
            "15.367559241130948\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180922-m7imp1fv/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The number of items in train is: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180922-m7imp1fv/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "26\n",
            "The loss for epoch 3\n",
            "0.5910599708127288\n",
            "2\n",
            "The running loss is:\n",
            "14.148938469588757\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.5441899411380291\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_09PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180922-m7imp1fv/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180922-m7imp1fv/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_09PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['m7imp1fv']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  440.134735\n",
            "66  2020-04-22  sub_region  ...             66  439.800903\n",
            "67  2020-04-23  sub_region  ...             67  443.283325\n",
            "68  2020-04-24  sub_region  ...             68  442.842438\n",
            "69  2020-04-25  sub_region  ...             69  440.653381\n",
            "70  2020-04-26  sub_region  ...             70  441.242767\n",
            "71  2020-04-27  sub_region  ...             71  440.577118\n",
            "72  2020-04-28  sub_region  ...             72  442.682800\n",
            "73  2020-04-29  sub_region  ...             73  441.382904\n",
            "74  2020-04-30  sub_region  ...             74  445.453522\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180922-m7imp1fv/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180922-m7imp1fv/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: m7imp1fv \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180922-m7imp1fv/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180922-m7imp1fv/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180922-m7imp1fv/media/plotly/test_plot_10_0d49373a.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180922-m7imp1fv/media/plotly/test_plot_all_11_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180922-m7imp1fv/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180922-m7imp1fv/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180922-m7imp1fv/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180922-m7imp1fv/wandb-events.jsonl\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: m7imp1fv\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: ecwpsoe9 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: ecwpsoe9\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/ecwpsoe9\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/ecwpsoe9</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/ecwpsoe9\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmVjd3Bzb2U5OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180934-ecwpsoe9/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180934-ecwpsoe9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180934-ecwpsoe9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180934-ecwpsoe9/media/graph/graph_0_summary_54d34cc7.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180934-ecwpsoe9/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180934-ecwpsoe9/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180934-ecwpsoe9/wandb-events.jsonl\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180934-ecwpsoe9/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "23.482627481222153\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.9031779800470059\n",
            "The running loss is:\n",
            "20.23513476550579\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.778274414057915\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180934-ecwpsoe9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180934-ecwpsoe9/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.649361565709114\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.8326677525272737\n",
            "The running loss is:\n",
            "15.995433270931244\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.615208971958894\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180934-ecwpsoe9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180934-ecwpsoe9/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "12.781472019851208\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.4915950776865849\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180934-ecwpsoe9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180934-ecwpsoe9/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.850842893123627\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.3404170343509087\n",
            "The running loss is:\n",
            "6.8902468085289\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.26500949263572693\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['ecwpsoe9']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180934-ecwpsoe9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180934-ecwpsoe9/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.579151637852192\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.32996737068662274\n",
            "2\n",
            "The running loss is:\n",
            "6.014725340530276\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.23133559002039525\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180934-ecwpsoe9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180934-ecwpsoe9/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.920796798542142\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.18926141532854393\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202006_09PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_09PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180934-ecwpsoe9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180934-ecwpsoe9/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180934-ecwpsoe9/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  784.844604\n",
            "66  2020-04-22  sub_region  ...             66  785.061829\n",
            "67  2020-04-23  sub_region  ...             67  785.951660\n",
            "68  2020-04-24  sub_region  ...             68  785.422729\n",
            "69  2020-04-25  sub_region  ...             69  784.747925\n",
            "70  2020-04-26  sub_region  ...             70  784.533691\n",
            "71  2020-04-27  sub_region  ...             71  784.408447\n",
            "72  2020-04-28  sub_region  ...             72  785.171936\n",
            "73  2020-04-29  sub_region  ...             73  784.649170\n",
            "74  2020-04-30  sub_region  ...             74  785.943665\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180934-ecwpsoe9/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180934-ecwpsoe9/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180934-ecwpsoe9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180934-ecwpsoe9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180934-ecwpsoe9/media/plotly/test_plot_20_bf33d510.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180934-ecwpsoe9/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180934-ecwpsoe9/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: ecwpsoe9 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180934-ecwpsoe9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180934-ecwpsoe9/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180934-ecwpsoe9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180934-ecwpsoe9/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180934-ecwpsoe9/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180934-ecwpsoe9/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: ecwpsoe9\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
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          "output_type": "stream",
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            "wandb: Agent Starting Run: 27b1l264 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 27b1l264\n"
          ],
          "name": "stdout"
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          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/27b1l264\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/27b1l264</a><br/>\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/27b1l264\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjI3YjFsMjY0OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
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          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180951-27b1l264/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180951-27b1l264/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180951-27b1l264/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180951-27b1l264/media/graph/graph_0_summary_9c42a37e.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180951-27b1l264/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180951-27b1l264/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180951-27b1l264/wandb-events.jsonl\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "23.31422958523035\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.896701137893475\n",
            "The running loss is:\n",
            "19.628748193383217\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.7549518535916622\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180951-27b1l264/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180951-27b1l264/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.03840383887291\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.8476309168797272\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180951-27b1l264/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180951-27b1l264/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.539160393178463\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.5976600151222485\n",
            "1\n",
            "The running loss is:\n",
            "12.818177118897438\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.4930068122652861\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180951-27b1l264/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180951-27b1l264/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.63734956085682\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.4091288292637238\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['27b1l264']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180951-27b1l264/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180951-27b1l264/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.949803344905376\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.2673001286502068\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180951-27b1l264/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180951-27b1l264/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.64347056671977\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.29397963718152964\n",
            "1\n",
            "The running loss is:\n",
            "11.507175385951996\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.4425836686904614\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180951-27b1l264/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180951-27b1l264/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.667488571256399\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.21798032966370767\n",
            "Data saved to: \n",
            "28_May_202006_09PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_09PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180951-27b1l264/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180951-27b1l264/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180951-27b1l264/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  749.537842\n",
            "66  2020-04-22  sub_region  ...             66  753.229736\n",
            "67  2020-04-23  sub_region  ...             67  753.515015\n",
            "68  2020-04-24  sub_region  ...             68  753.205444\n",
            "69  2020-04-25  sub_region  ...             69  751.122925\n",
            "70  2020-04-26  sub_region  ...             70  750.473755\n",
            "71  2020-04-27  sub_region  ...             71  748.162842\n",
            "72  2020-04-28  sub_region  ...             72  752.245361\n",
            "73  2020-04-29  sub_region  ...             73  750.474243\n",
            "74  2020-04-30  sub_region  ...             74  754.320679\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180951-27b1l264/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180951-27b1l264/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 27b1l264 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180951-27b1l264/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180951-27b1l264/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180951-27b1l264/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180951-27b1l264/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180951-27b1l264/media/plotly/test_plot_20_67e12e5e.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180951-27b1l264/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180951-27b1l264/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_180951-27b1l264/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 27b1l264\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 8g69sz1i with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 8g69sz1i\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/8g69sz1i\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/8g69sz1i</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/8g69sz1i\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjhnNjlzejFpOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181008-8g69sz1i/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181008-8g69sz1i/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181008-8g69sz1i/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181008-8g69sz1i/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181008-8g69sz1i/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181008-8g69sz1i/media/graph/graph_0_summary_1c2050d9.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181008-8g69sz1i/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181008-8g69sz1i/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181008-8g69sz1i/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.830005079507828\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.7132002031803131\n",
            "The running loss is:\n",
            "16.9639795422554\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.6785591816902161\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181008-8g69sz1i/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181008-8g69sz1i/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.417406290769577\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.776696251630783\n",
            "The running loss is:\n",
            "13.737709395587444\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.5495083758234978\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181008-8g69sz1i/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181008-8g69sz1i/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "13.103170596063137\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.5241268238425255\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181008-8g69sz1i/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181008-8g69sz1i/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.996175795793533\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.3998470318317413\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_10PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_10PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['8g69sz1i']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181008-8g69sz1i/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  417.858063\n",
            "66  2020-04-22  sub_region  ...             66  419.026245\n",
            "67  2020-04-23  sub_region  ...             67  420.017090\n",
            "68  2020-04-24  sub_region  ...             68  420.572571\n",
            "69  2020-04-25  sub_region  ...             69  419.899597\n",
            "70  2020-04-26  sub_region  ...             70  419.641113\n",
            "71  2020-04-27  sub_region  ...             71  419.378143\n",
            "72  2020-04-28  sub_region  ...             72  419.695251\n",
            "73  2020-04-29  sub_region  ...             73  419.175232\n",
            "74  2020-04-30  sub_region  ...             74  421.516815\n",
            "\n",
            "[21 rows x 28 columns]"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181008-8g69sz1i/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181008-8g69sz1i/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181008-8g69sz1i/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181008-8g69sz1i/wandb-summary.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181008-8g69sz1i/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181008-8g69sz1i/media/plotly/test_plot_12_d17e53a2.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181008-8g69sz1i/media\n",
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            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
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            "wandb: Agent Finished Run: 8g69sz1i \n",
            "\n"
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181008-8g69sz1i/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181008-8g69sz1i/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181008-8g69sz1i/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181008-8g69sz1i/media/plotly/test_plot_all_13_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181008-8g69sz1i/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 8g69sz1i\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
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          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: xxokbmwc with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: xxokbmwc\n"
          ],
          "name": "stdout"
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          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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        {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/xxokbmwc\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/xxokbmwc</a><br/>\n",
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            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
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            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnh4b2tibXdjOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
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          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
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        },
        {
          "output_type": "stream",
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181020-xxokbmwc/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181020-xxokbmwc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181020-xxokbmwc/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181020-xxokbmwc/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181020-xxokbmwc/media/graph/graph_0_summary_6547bba0.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181020-xxokbmwc/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181020-xxokbmwc/wandb-events.jsonl\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181020-xxokbmwc/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "17.741086795926094\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.7096434718370438\n",
            "The running loss is:\n",
            "16.730679839849472\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.6692271935939789\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181020-xxokbmwc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181020-xxokbmwc/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.939407482743263\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.7975762993097305\n",
            "The running loss is:\n",
            "13.255669929087162\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.5302267971634865\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181020-xxokbmwc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181020-xxokbmwc/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.027616888284683\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.4411046755313873\n",
            "2\n",
            "The running loss is:\n",
            "6.718288138508797\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.26873152554035185\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181020-xxokbmwc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181020-xxokbmwc/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.313029149547219\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.29252116598188876\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['xxokbmwc']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181020-xxokbmwc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181020-xxokbmwc/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.773601979017258\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.5109440791606903\n",
            "The running loss is:\n",
            "6.80379575304687\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.2721518301218748\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181020-xxokbmwc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181020-xxokbmwc/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.739170776680112\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.18956683106720448\n",
            "2\n",
            "Data saved to: \n",
            "28_May_202006_10PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_10PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181020-xxokbmwc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181020-xxokbmwc/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181020-xxokbmwc/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  770.853760\n",
            "66  2020-04-22  sub_region  ...             66  769.976440\n",
            "67  2020-04-23  sub_region  ...             67  771.220154\n",
            "68  2020-04-24  sub_region  ...             68  770.911987\n",
            "69  2020-04-25  sub_region  ...             69  770.517700\n",
            "70  2020-04-26  sub_region  ...             70  770.186768\n",
            "71  2020-04-27  sub_region  ...             71  770.193970\n",
            "72  2020-04-28  sub_region  ...             72  770.157593\n",
            "73  2020-04-29  sub_region  ...             73  770.232788\n",
            "74  2020-04-30  sub_region  ...             74  770.960083\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181020-xxokbmwc/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181020-xxokbmwc/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181020-xxokbmwc/wandb-history.jsonl\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181020-xxokbmwc/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181020-xxokbmwc/media/plotly/test_plot_20_57ec849c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181020-xxokbmwc/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181020-xxokbmwc/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: xxokbmwc \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181020-xxokbmwc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181020-xxokbmwc/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181020-xxokbmwc/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181020-xxokbmwc/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181020-xxokbmwc/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181020-xxokbmwc/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: xxokbmwc\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: srirmbfg with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: srirmbfg\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/srirmbfg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/srirmbfg</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/srirmbfg\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnNyaXJtYmZnOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/media/graph/graph_0_summary_236fd991.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.64892402663827\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.5055157046903063\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "37.692751033231616\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.3960278160456154\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.47567129507661\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7583581961139485\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.933722829446197\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.7753230677572666\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['srirmbfg']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.798208009451628\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.7703040003500603\n",
            "The running loss is:\n",
            "20.184429235756397\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.7475714531761629\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "20.411993622779846\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.755999763806661\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.94804622977972\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.7388165270288786\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_10PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_10PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  474.087341\n",
            "66  2020-04-22  sub_region  ...             66  474.107361\n",
            "67  2020-04-23  sub_region  ...             67  473.755005\n",
            "68  2020-04-24  sub_region  ...             68  474.003113\n",
            "69  2020-04-25  sub_region  ...             69  474.199402\n",
            "70  2020-04-26  sub_region  ...             70  474.221405\n",
            "71  2020-04-27  sub_region  ...             71  474.191833\n",
            "72  2020-04-28  sub_region  ...             72  473.996887\n",
            "73  2020-04-29  sub_region  ...             73  474.185181\n",
            "74  2020-04-30  sub_region  ...             74  473.697998\n",
            "\n",
            "[21 rows x 28 columns]"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/media/plotly/test_plot_16_1f8d7214.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: srirmbfg \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/media/plotly/test_plot_all_17_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181037-srirmbfg/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: srirmbfg\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: t4xosvtx with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: t4xosvtx\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/t4xosvtx\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/t4xosvtx</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/t4xosvtx\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnQ0eG9zdnR4OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181058-t4xosvtx/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181058-t4xosvtx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181058-t4xosvtx/media/graph/graph_0_summary_453d443c.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181058-t4xosvtx/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181058-t4xosvtx/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181058-t4xosvtx/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181058-t4xosvtx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181058-t4xosvtx/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181058-t4xosvtx/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.37478191871196\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.49536229328562814\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181058-t4xosvtx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181058-t4xosvtx/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "37.45775743201375\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.3873243493338425\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181058-t4xosvtx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181058-t4xosvtx/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.595209177583456\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.7627855250956835\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181058-t4xosvtx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181058-t4xosvtx/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.006036780774593\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.7780013622509109\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['t4xosvtx']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181058-t4xosvtx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181058-t4xosvtx/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.73020259477198\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.7677852812878512\n",
            "The running loss is:\n",
            "20.24301341921091\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.7497412377485523\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181058-t4xosvtx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181058-t4xosvtx/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "20.167157787829638\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.7469317699196162\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181058-t4xosvtx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181058-t4xosvtx/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.895125970244408\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 7\n",
            "0.6998194803794225\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_11PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181058-t4xosvtx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181058-t4xosvtx/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_11PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  474.395355\n",
            "66  2020-04-22  sub_region  ...             66  474.474243\n",
            "67  2020-04-23  sub_region  ...             67  473.946503\n",
            "68  2020-04-24  sub_region  ...             68  474.251740\n",
            "69  2020-04-25  sub_region  ...             69  474.498688\n",
            "70  2020-04-26  sub_region  ...             70  474.531128\n",
            "71  2020-04-27  sub_region  ...             71  474.499969\n",
            "72  2020-04-28  sub_region  ...             72  474.257050\n",
            "73  2020-04-29  sub_region  ...             73  474.527222\n",
            "74  2020-04-30  sub_region  ...             74  473.818420\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181058-t4xosvtx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181058-t4xosvtx/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: t4xosvtx \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181058-t4xosvtx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181058-t4xosvtx/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181058-t4xosvtx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181058-t4xosvtx/media/plotly/test_plot_all_17_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181058-t4xosvtx/media/plotly/test_plot_16_c257944a.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181058-t4xosvtx/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181058-t4xosvtx/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181058-t4xosvtx/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: t4xosvtx\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: clrrnsx0 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: clrrnsx0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/clrrnsx0\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/clrrnsx0</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/clrrnsx0\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmNscnJuc3gwOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181114-clrrnsx0/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181114-clrrnsx0/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181114-clrrnsx0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181114-clrrnsx0/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181114-clrrnsx0/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181114-clrrnsx0/media/graph/graph_0_summary_1eb04cf7.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181114-clrrnsx0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181114-clrrnsx0/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181114-clrrnsx0/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.137208757922053\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.5606373614045205\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181114-clrrnsx0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181114-clrrnsx0/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "30.88287841156125\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.1438103115393057\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181114-clrrnsx0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181114-clrrnsx0/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "25.371903955936432\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.9397001465161642\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181114-clrrnsx0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181114-clrrnsx0/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.891498629003763\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.7737592084816208\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['clrrnsx0']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.58536821603775\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.7624210450384352\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181114-clrrnsx0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181114-clrrnsx0/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "21.16692252457142\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.7839600935026452\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181114-clrrnsx0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181114-clrrnsx0/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.775313809514046\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.7694560670190387\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_11PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181114-clrrnsx0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181114-clrrnsx0/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_11PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  422.063049\n",
            "66  2020-04-22  sub_region  ...             66  422.065277\n",
            "67  2020-04-23  sub_region  ...             67  422.008148\n",
            "68  2020-04-24  sub_region  ...             68  422.044128\n",
            "69  2020-04-25  sub_region  ...             69  422.075989\n",
            "70  2020-04-26  sub_region  ...             70  422.085114\n",
            "71  2020-04-27  sub_region  ...             71  422.084900\n",
            "72  2020-04-28  sub_region  ...             72  422.078888\n",
            "73  2020-04-29  sub_region  ...             73  422.060028\n",
            "74  2020-04-30  sub_region  ...             74  422.037537\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181114-clrrnsx0/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181114-clrrnsx0/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181114-clrrnsx0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181114-clrrnsx0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181114-clrrnsx0/media/plotly/test_plot_14_7a77c25e.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181114-clrrnsx0/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181114-clrrnsx0/media/plotly\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: clrrnsx0 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181114-clrrnsx0/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181114-clrrnsx0/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181114-clrrnsx0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181114-clrrnsx0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181114-clrrnsx0/media/plotly/test_plot_all_15_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181114-clrrnsx0/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: clrrnsx0\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: aiej4x17 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: aiej4x17\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/aiej4x17\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/aiej4x17</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/aiej4x17\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmFpZWo0eDE3OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181131-aiej4x17/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181131-aiej4x17/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181131-aiej4x17/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181131-aiej4x17/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181131-aiej4x17/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181131-aiej4x17/media/graph/graph_0_summary_83359f6b.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181131-aiej4x17/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181131-aiej4x17/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181131-aiej4x17/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.071377269923687\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 0\n",
            "0.5581991581453217\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181131-aiej4x17/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181131-aiej4x17/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "31.568736284971237\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 1\n",
            "1.1692124549989347\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181131-aiej4x17/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181131-aiej4x17/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "25.198651179671288\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 2\n",
            "0.9332833770248625\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181131-aiej4x17/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181131-aiej4x17/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.102718703448772\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 3\n",
            "0.7815821742018064\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['aiej4x17']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.57193885743618\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 4\n",
            "0.7619236613865252\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181131-aiej4x17/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181131-aiej4x17/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "21.114420972764492\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 5\n",
            "0.7820155915838701\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181131-aiej4x17/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181131-aiej4x17/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.067522056400776\n",
            "The number of items in train is: \n",
            "27\n",
            "The loss for epoch 6\n",
            "0.7802785946815102\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_11PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181131-aiej4x17/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181131-aiej4x17/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_11PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  423.483063\n",
            "66  2020-04-22  sub_region  ...             66  423.511169\n",
            "67  2020-04-23  sub_region  ...             67  423.472900\n",
            "68  2020-04-24  sub_region  ...             68  423.498932\n",
            "69  2020-04-25  sub_region  ...             69  423.520294\n",
            "70  2020-04-26  sub_region  ...             70  423.524902\n",
            "71  2020-04-27  sub_region  ...             71  423.520996\n",
            "72  2020-04-28  sub_region  ...             72  423.510651\n",
            "73  2020-04-29  sub_region  ...             73  423.502411\n",
            "74  2020-04-30  sub_region  ...             74  423.487152\n",
            "\n",
            "[21 rows x 28 columns]"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181131-aiej4x17/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181131-aiej4x17/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181131-aiej4x17/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181131-aiej4x17/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181131-aiej4x17/media/plotly/test_plot_14_0bb7c432.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181131-aiej4x17/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181131-aiej4x17/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: aiej4x17 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181131-aiej4x17/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181131-aiej4x17/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181131-aiej4x17/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181131-aiej4x17/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181131-aiej4x17/media/plotly/test_plot_all_15_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181131-aiej4x17/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: aiej4x17\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: bt7r2ilt with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: bt7r2ilt\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/bt7r2ilt\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/bt7r2ilt</a><br/>\n",
              "            "
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            "DEBUG:wandb.meta:probe for git information\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/bt7r2ilt\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmJ0N3IyaWx0OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/wandb-summary.json\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "24.238217145204544\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.9322391209694055\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.11776690930128\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.6199141118962032\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.947837613523006\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.6518399082124233\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.64614214003086\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.6786977746165715\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['bt7r2ilt']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.301386021077633\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.6269763854260628\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.042810605838895\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.6170311771476498\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.744363751262426\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.6055524519716318\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.016306111589074\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.6160117735226567\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.813427444547415\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.6082087478672082\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.24589281156659\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 9\n",
            "0.6248420312140996\n",
            "Data saved to: \n",
            "28_May_202006_11PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_11PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  509.934052\n",
            "66  2020-04-22  sub_region  ...             66  507.839417\n",
            "67  2020-04-23  sub_region  ...             67  509.408234\n",
            "68  2020-04-24  sub_region  ...             68  509.694031\n",
            "69  2020-04-25  sub_region  ...             69  509.575317\n",
            "70  2020-04-26  sub_region  ...             70  508.830750\n",
            "71  2020-04-27  sub_region  ...             71  509.065674\n",
            "72  2020-04-28  sub_region  ...             72  507.174622\n",
            "73  2020-04-29  sub_region  ...             73  509.588074\n",
            "74  2020-04-30  sub_region  ...             74  507.856110\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/media/plotly/test_plot_20_75179c3f.plotly.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: bt7r2ilt \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181149-bt7r2ilt/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: bt7r2ilt\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: d4g2l0ez with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: d4g2l0ez\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/d4g2l0ez\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/d4g2l0ez</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/d4g2l0ez\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmQ0ZzJsMGV6OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/media/graph/graph_0_summary_1e552aae.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "24.46081606298685\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.9408006178071866\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.266936726868153\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.625651412571852\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.894336327910423\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.6497821664580932\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.256735399365425\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.6637205922832856\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['d4g2l0ez']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.231802832335234\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.6243001089359705\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.1003286074847\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.6192434079801807\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.810061607509851\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 6\n",
            "0.6080792925965327\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.048187194392085\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 7\n",
            "0.6172379690150802\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.809576485306025\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 8\n",
            "0.6080606340502317\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_12PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_12PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  529.040771\n",
            "66  2020-04-22  sub_region  ...             66  528.143555\n",
            "67  2020-04-23  sub_region  ...             67  528.838806\n",
            "68  2020-04-24  sub_region  ...             68  528.961365\n",
            "69  2020-04-25  sub_region  ...             69  528.801025\n",
            "70  2020-04-26  sub_region  ...             70  528.515015\n",
            "71  2020-04-27  sub_region  ...             71  528.581177\n",
            "72  2020-04-28  sub_region  ...             72  528.077698\n",
            "73  2020-04-29  sub_region  ...             73  528.772461\n",
            "74  2020-04-30  sub_region  ...             74  528.409302\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/media/plotly/test_plot_18_712b0439.plotly.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: d4g2l0ez \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/media/plotly/test_plot_all_19_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181215-d4g2l0ez/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: d4g2l0ez\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 89ypi0wm with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 89ypi0wm\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/89ypi0wm\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/89ypi0wm</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/89ypi0wm\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjg5eXBpMHdtOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181237-89ypi0wm/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181237-89ypi0wm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181237-89ypi0wm/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181237-89ypi0wm/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181237-89ypi0wm/media/graph/graph_0_summary_7aec0b6e.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181237-89ypi0wm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181237-89ypi0wm/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181237-89ypi0wm/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181237-89ypi0wm/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "24.921816915273666\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.958531419818218\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181237-89ypi0wm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181237-89ypi0wm/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.33100463449955\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.858884793634598\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181237-89ypi0wm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181237-89ypi0wm/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.428308714181185\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.7857041813146609\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181237-89ypi0wm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181237-89ypi0wm/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.427796185016632\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.6702998532698705\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['89ypi0wm']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.2829820625484\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.6647300793287846\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181237-89ypi0wm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181237-89ypi0wm/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "2\n",
            "The running loss is:\n",
            "15.887216325849295\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.6110467817634344\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_12PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181237-89ypi0wm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181237-89ypi0wm/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_12PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  559.779724\n",
            "66  2020-04-22  sub_region  ...             66  560.009094\n",
            "67  2020-04-23  sub_region  ...             67  560.145813\n",
            "68  2020-04-24  sub_region  ...             68  560.165466\n",
            "69  2020-04-25  sub_region  ...             69  560.001709\n",
            "70  2020-04-26  sub_region  ...             70  560.061584\n",
            "71  2020-04-27  sub_region  ...             71  559.992859\n",
            "72  2020-04-28  sub_region  ...             72  560.228088\n",
            "73  2020-04-29  sub_region  ...             73  559.984314\n",
            "74  2020-04-30  sub_region  ...             74  560.468567\n",
            "\n",
            "[21 rows x 28 columns]"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181237-89ypi0wm/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181237-89ypi0wm/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181237-89ypi0wm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181237-89ypi0wm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181237-89ypi0wm/media/plotly/test_plot_12_f72c3f2f.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181237-89ypi0wm/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181237-89ypi0wm/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 89ypi0wm \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181237-89ypi0wm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181237-89ypi0wm/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181237-89ypi0wm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181237-89ypi0wm/media/plotly/test_plot_all_13_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181237-89ypi0wm/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181237-89ypi0wm/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 89ypi0wm\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: a9hey38z with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: a9hey38z\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/a9hey38z\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/a9hey38z</a><br/>\n",
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        {
          "output_type": "stream",
          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/a9hey38z\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmE5aGV5Mzh6OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181254-a9hey38z/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181254-a9hey38z/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181254-a9hey38z/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181254-a9hey38z/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181254-a9hey38z/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181254-a9hey38z/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181254-a9hey38z/media/graph/graph_0_summary_8cd356a7.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181254-a9hey38z/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181254-a9hey38z/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "25.268363758921623\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 0\n",
            "0.9718601445739086\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181254-a9hey38z/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181254-a9hey38z/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.410943418741226\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 1\n",
            "0.8619593622592779\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181254-a9hey38z/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181254-a9hey38z/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.349964793771505\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 2\n",
            "0.7826909536065964\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181254-a9hey38z/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181254-a9hey38z/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.21460761502385\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 3\n",
            "0.6621002928855327\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['a9hey38z']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.72370159626007\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 4\n",
            "0.6816808306253873\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181254-a9hey38z/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181254-a9hey38z/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.540141738951206\n",
            "The number of items in train is: \n",
            "26\n",
            "The loss for epoch 5\n",
            "0.597697759190431\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_13PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181254-a9hey38z/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181254-a9hey38z/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_13PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  559.067444\n",
            "66  2020-04-22  sub_region  ...             66  559.143799\n",
            "67  2020-04-23  sub_region  ...             67  559.442505\n",
            "68  2020-04-24  sub_region  ...             68  559.442566\n",
            "69  2020-04-25  sub_region  ...             69  559.225464\n",
            "70  2020-04-26  sub_region  ...             70  559.264404\n",
            "71  2020-04-27  sub_region  ...             71  559.198975\n",
            "72  2020-04-28  sub_region  ...             72  559.425171\n",
            "73  2020-04-29  sub_region  ...             73  559.358643\n",
            "74  2020-04-30  sub_region  ...             74  559.806274\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181254-a9hey38z/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181254-a9hey38z/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181254-a9hey38z/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181254-a9hey38z/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181254-a9hey38z/media/plotly/test_plot_12_b8d9f787.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181254-a9hey38z/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181254-a9hey38z/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: a9hey38z \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181254-a9hey38z/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181254-a9hey38z/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181254-a9hey38z/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181254-a9hey38z/media/plotly/test_plot_all_13_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181254-a9hey38z/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181254-a9hey38z/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: a9hey38z\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: xd7cpvm3 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: xd7cpvm3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/xd7cpvm3\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/xd7cpvm3</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/xd7cpvm3\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnhkN2Nwdm0zOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 11\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/media/graph/graph_0_summary_ac6126e9.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.4773468375206\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.819093873500824\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.37137946486473\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.7748551785945892\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.317528434097767\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.6527011373639107\n",
            "The running loss is:\n",
            "14.260967433452606\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.5704386973381043\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['xd7cpvm3']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "14.17140232771635\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.566856093108654\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.054600022733212\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.5621840009093284\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/wandb-summary.json\n"
          ],
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        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.07607826590538\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.5630431306362152\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.025826334953308\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.5610330533981324\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.500305883586407\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.5400122353434562\n",
            "2\n",
            "The running loss is:\n",
            "12.7346723228693\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.509386892914772\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_13PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_13PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  384.512299\n",
            "66  2020-04-22  sub_region  ...             66  383.916504\n",
            "67  2020-04-23  sub_region  ...             67  384.334045\n",
            "68  2020-04-24  sub_region  ...             68  384.549500\n",
            "69  2020-04-25  sub_region  ...             69  384.431915\n",
            "70  2020-04-26  sub_region  ...             70  384.146332\n",
            "71  2020-04-27  sub_region  ...             71  384.198578\n",
            "72  2020-04-28  sub_region  ...             72  383.738586\n",
            "73  2020-04-29  sub_region  ...             73  384.381348\n",
            "74  2020-04-30  sub_region  ...             74  383.990662\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/wandb-history.jsonl\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/media/plotly/test_plot_20_1987c841.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: xd7cpvm3 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/media/plotly/test_plot_all_21_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181311-xd7cpvm3/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: xd7cpvm3\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: lt4usxjg with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 11\n",
            "\tlr: 0.01\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: lt4usxjg\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/lt4usxjg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/lt4usxjg</a><br/>\n",
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            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
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          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 11\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 11\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 11\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.01\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
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            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 11\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.01\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
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          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181335-lt4usxjg/config.yaml\n",
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          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.519908547401428\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.8207963418960571\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181335-lt4usxjg/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181335-lt4usxjg/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.348501980304718\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.7739400792121888\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181335-lt4usxjg/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181335-lt4usxjg/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.020245015621185\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.6408098006248474\n",
            "The running loss is:\n",
            "14.293965496122837\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.5717586198449135\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181335-lt4usxjg/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181335-lt4usxjg/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "14.129456043243408\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.5651782417297363\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['lt4usxjg']\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181335-lt4usxjg/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181335-lt4usxjg/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.794427633285522\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.5517771053314209\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181335-lt4usxjg/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181335-lt4usxjg/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.02604092657566\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.5210416370630264\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181335-lt4usxjg/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181335-lt4usxjg/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.591085441410542\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.3836434176564217\n",
            "2\n",
            "The running loss is:\n",
            "7.496750168502331\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.29987000674009323\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_13PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181335-lt4usxjg/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181335-lt4usxjg/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_13PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 11, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181335-lt4usxjg/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  388.992981\n",
            "66  2020-04-22  sub_region  ...             66  387.531372\n",
            "67  2020-04-23  sub_region  ...             67  388.493744\n",
            "68  2020-04-24  sub_region  ...             68  388.772583\n",
            "69  2020-04-25  sub_region  ...             69  388.547852\n",
            "70  2020-04-26  sub_region  ...             70  387.934692\n",
            "71  2020-04-27  sub_region  ...             71  388.074402\n",
            "72  2020-04-28  sub_region  ...             72  387.295563\n",
            "73  2020-04-29  sub_region  ...             73  388.444427\n",
            "74  2020-04-30  sub_region  ...             74  387.736328\n",
            "\n",
            "[21 rows x 28 columns]\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181335-lt4usxjg/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181335-lt4usxjg/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181335-lt4usxjg/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181335-lt4usxjg/media/plotly/test_plot_18_166901d4.plotly.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181335-lt4usxjg/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181335-lt4usxjg/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: lt4usxjg \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181335-lt4usxjg/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181335-lt4usxjg/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181335-lt4usxjg/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181335-lt4usxjg/media/plotly/test_plot_all_19_6655637c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181335-lt4usxjg/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181335-lt4usxjg/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: lt4usxjg\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: d6ex9o8g with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: d6ex9o8g\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/d6ex9o8g\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/d6ex9o8g</a><br/>\n",
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/d6ex9o8g\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmQ2ZXg5bzhnOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "13.29942782886792\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.5319771131547167\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181352-d6ex9o8g/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181352-d6ex9o8g/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181352-d6ex9o8g/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181352-d6ex9o8g/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181352-d6ex9o8g/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181352-d6ex9o8g/media/graph/graph_0_summary_12bf24e1.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181352-d6ex9o8g/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181352-d6ex9o8g/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181352-d6ex9o8g/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.958505789749324\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.638340231589973\n",
            "The running loss is:\n",
            "9.610189565457404\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.38440758261829616\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181352-d6ex9o8g/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181352-d6ex9o8g/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.331762389920186\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.33327049559680744\n",
            "1\n",
            "The running loss is:\n",
            "6.60916328427993\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.2643665313711972\n",
            "2\n",
            "The running loss is:\n",
            "6.482465127395699\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.25929860509582797\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181352-d6ex9o8g/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181352-d6ex9o8g/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.1515498894732445\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.2860619955789298\n",
            "The running loss is:\n",
            "6.881212463777047\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.27524849855108185\n",
            "The running loss is:\n",
            "7.631635777652264\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.30526543110609056\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181352-d6ex9o8g/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181352-d6ex9o8g/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.2557811347651295\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.2502312453906052\n",
            "Data saved to: \n",
            "28_May_202006_13PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_13PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['d6ex9o8g']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181352-d6ex9o8g/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181352-d6ex9o8g/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181352-d6ex9o8g/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  801.962341\n",
            "66  2020-04-22  sub_region  ...             66  824.838013\n",
            "67  2020-04-23  sub_region  ...             67  775.098450\n",
            "68  2020-04-24  sub_region  ...             68  820.728394\n",
            "69  2020-04-25  sub_region  ...             69  858.230591\n",
            "70  2020-04-26  sub_region  ...             70  790.655151\n",
            "71  2020-04-27  sub_region  ...             71  957.766296\n",
            "72  2020-04-28  sub_region  ...             72  793.469360\n",
            "73  2020-04-29  sub_region  ...             73  769.994263\n",
            "74  2020-04-30  sub_region  ...             74  771.788574\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181352-d6ex9o8g/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181352-d6ex9o8g/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181352-d6ex9o8g/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181352-d6ex9o8g/wandb-history.jsonl\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181352-d6ex9o8g/media\n",
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            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
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          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: d6ex9o8g \n",
            "\n"
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181352-d6ex9o8g/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181352-d6ex9o8g/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181352-d6ex9o8g/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181352-d6ex9o8g/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181352-d6ex9o8g/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: d6ex9o8g\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
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          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 3igvo891 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 3igvo891\n"
          ],
          "name": "stdout"
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          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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        {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/3igvo891\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/3igvo891</a><br/>\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/3igvo891\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
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            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjNpZ3ZvODkxOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
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        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "13.510150267975405\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.5404060107190162\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181410-3igvo891/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181410-3igvo891/wandb-events.jsonl\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181410-3igvo891/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181410-3igvo891/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181410-3igvo891/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181410-3igvo891/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181410-3igvo891/media\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.415556019172072\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.6566222407668829\n",
            "1\n",
            "The running loss is:\n",
            "8.791841979138553\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.35167367916554215\n",
            "The running loss is:\n",
            "9.764055475010537\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.3905622190004215\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181410-3igvo891/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181410-3igvo891/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.065425713779405\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.2826170285511762\n",
            "1\n",
            "The running loss is:\n",
            "6.248815768616623\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.24995263074466492\n",
            "The running loss is:\n",
            "6.674299073871225\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.266971962954849\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181410-3igvo891/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181410-3igvo891/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "7.030096196685918\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.2812038478674367\n",
            "The running loss is:\n",
            "8.11010273359716\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.32440410934388636\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181410-3igvo891/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181410-3igvo891/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.296146783512086\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.33184587134048343\n",
            "Data saved to: \n",
            "28_May_202006_14PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_14PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['3igvo891']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181410-3igvo891/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181410-3igvo891/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181410-3igvo891/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 3igvo891 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181410-3igvo891/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181410-3igvo891/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181410-3igvo891/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181410-3igvo891/media/plotly/test_plot_20_f6cc8bc8.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181410-3igvo891/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181410-3igvo891/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181410-3igvo891/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181410-3igvo891/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 3igvo891\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 2ptjpclj with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 2ptjpclj\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/2ptjpclj\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/2ptjpclj</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/2ptjpclj\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjJwdGpwY2xqOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
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          ],
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        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "19.00246888399124\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.7600987553596497\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181422-2ptjpclj/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181422-2ptjpclj/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181422-2ptjpclj/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181422-2ptjpclj/media/graph/graph_0_summary_6986d7e9.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181422-2ptjpclj/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181422-2ptjpclj/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181422-2ptjpclj/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181422-2ptjpclj/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181422-2ptjpclj/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.036757367663085\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.6414702947065234\n",
            "1\n",
            "The running loss is:\n",
            "10.140504654031247\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.4056201861612499\n",
            "The running loss is:\n",
            "11.365801957435906\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.45463207829743624\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181422-2ptjpclj/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181422-2ptjpclj/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "15.48135045915842\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.6192540183663369\n",
            "2\n",
            "The running loss is:\n",
            "8.226837230846286\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.32907348923385144\n",
            "The running loss is:\n",
            "7.584667568095028\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.30338670272380114\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181422-2ptjpclj/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181422-2ptjpclj/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.201896103098989\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.3280758441239595\n",
            "2\n",
            "The running loss is:\n",
            "8.102215321734548\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.3240886128693819\n",
            "The running loss is:\n",
            "8.849566355347633\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.35398265421390535\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181422-2ptjpclj/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181422-2ptjpclj/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "Data saved to: \n",
            "28_May_202006_14PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_14PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['2ptjpclj']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181422-2ptjpclj/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181422-2ptjpclj/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181422-2ptjpclj/config.yaml\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  721.418884\n",
            "66  2020-04-22  sub_region  ...             66  729.418823\n",
            "67  2020-04-23  sub_region  ...             67  704.071716\n",
            "68  2020-04-24  sub_region  ...             68  698.017456\n",
            "69  2020-04-25  sub_region  ...             69  721.563843\n",
            "70  2020-04-26  sub_region  ...             70  702.487244\n",
            "71  2020-04-27  sub_region  ...             71  729.263550\n",
            "72  2020-04-28  sub_region  ...             72  726.579834\n",
            "73  2020-04-29  sub_region  ...             73  722.222656\n",
            "74  2020-04-30  sub_region  ...             74  718.942139\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181422-2ptjpclj/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181422-2ptjpclj/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181422-2ptjpclj/wandb-history.jsonl\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181422-2ptjpclj/media/plotly/test_plot_20_359c7a7b.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181422-2ptjpclj/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181422-2ptjpclj/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 2ptjpclj \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181422-2ptjpclj/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181422-2ptjpclj/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181422-2ptjpclj/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181422-2ptjpclj/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181422-2ptjpclj/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181422-2ptjpclj/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 2ptjpclj\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: jcdp0taa with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: jcdp0taa\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/jcdp0taa\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/jcdp0taa</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/jcdp0taa\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmpjZHAwdGFhOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "18.724818095564842\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.7489927238225937\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181439-jcdp0taa/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181439-jcdp0taa/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181439-jcdp0taa/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181439-jcdp0taa/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181439-jcdp0taa/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181439-jcdp0taa/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181439-jcdp0taa/media/graph/graph_0_summary_05e489c0.graph.json\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181439-jcdp0taa/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.727116253226995\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.6290846501290798\n",
            "1\n",
            "The running loss is:\n",
            "8.56138514901977\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.3424554059607908\n",
            "The running loss is:\n",
            "10.345093304291368\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.4138037321716547\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181439-jcdp0taa/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181439-jcdp0taa/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "10.967490065842867\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.43869960263371466\n",
            "The running loss is:\n",
            "7.611721908673644\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.30446887634694575\n",
            "The running loss is:\n",
            "7.797869371250272\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.31191477485001085\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181439-jcdp0taa/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181439-jcdp0taa/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.795289715752006\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.31181158863008024\n",
            "The running loss is:\n",
            "6.245977671816945\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.2498391068726778\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181439-jcdp0taa/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181439-jcdp0taa/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.67815606854856\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.2671262427419424\n",
            "Data saved to: \n",
            "28_May_202006_14PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_14PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['jcdp0taa']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181439-jcdp0taa/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181439-jcdp0taa/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181439-jcdp0taa/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  648.033325\n",
            "66  2020-04-22  sub_region  ...             66  696.399780\n",
            "67  2020-04-23  sub_region  ...             67  633.036682\n",
            "68  2020-04-24  sub_region  ...             68  599.434021\n",
            "69  2020-04-25  sub_region  ...             69  679.843323\n",
            "70  2020-04-26  sub_region  ...             70  630.065979\n",
            "71  2020-04-27  sub_region  ...             71  673.871094\n",
            "72  2020-04-28  sub_region  ...             72  681.052856\n",
            "73  2020-04-29  sub_region  ...             73  670.992432\n",
            "74  2020-04-30  sub_region  ...             74  661.747437\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181439-jcdp0taa/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181439-jcdp0taa/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181439-jcdp0taa/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181439-jcdp0taa/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181439-jcdp0taa/media/plotly/test_plot_20_c2722b2a.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181439-jcdp0taa/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181439-jcdp0taa/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: jcdp0taa \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181439-jcdp0taa/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181439-jcdp0taa/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181439-jcdp0taa/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181439-jcdp0taa/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181439-jcdp0taa/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181439-jcdp0taa/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: jcdp0taa\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: sfn581er with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: sfn581er\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/sfn581er\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/sfn581er</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/sfn581er\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnNmbjU4MWVyOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "12.417334482073784\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 0\n",
            "0.5173889367530743\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181459-sfn581er/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181459-sfn581er/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181459-sfn581er/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181459-sfn581er/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181459-sfn581er/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181459-sfn581er/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181459-sfn581er/media/graph/graph_0_summary_e93bba15.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181459-sfn581er/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181459-sfn581er/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.187367584556341\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.5911403160231808\n",
            "The running loss is:\n",
            "8.399394869804382\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.34997478624184925\n",
            "1\n",
            "The running loss is:\n",
            "6.990775217302144\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 3\n",
            "0.29128230072092265\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181459-sfn581er/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181459-sfn581er/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.946204421576113\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 4\n",
            "0.37275851756567135\n",
            "1\n",
            "The running loss is:\n",
            "15.735674642026424\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n",
            "0.6556531100844344\n",
            "The running loss is:\n",
            "7.7431636694818735\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 6\n",
            "0.32263181956174475\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181459-sfn581er/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181459-sfn581er/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "4.328124134801328\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 7\n",
            "0.18033850561672202\n",
            "2\n",
            "The running loss is:\n",
            "3.735455541405827\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 8\n",
            "0.15564398089190945\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181459-sfn581er/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181459-sfn581er/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "3.9662495320662856\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 9\n",
            "0.16526039716942856\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202006_15PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_15PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['sfn581er']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  755.517212\n",
            "66  2020-04-22  sub_region  ...             66  771.916016\n",
            "67  2020-04-23  sub_region  ...             67  760.687378\n",
            "68  2020-04-24  sub_region  ...             68  735.305786\n",
            "69  2020-04-25  sub_region  ...             69  728.676880\n",
            "70  2020-04-26  sub_region  ...             70  742.574097\n",
            "71  2020-04-27  sub_region  ...             71  690.534424\n",
            "72  2020-04-28  sub_region  ...             72  759.479492\n",
            "73  2020-04-29  sub_region  ...             73  761.484863\n",
            "74  2020-04-30  sub_region  ...             74  765.487183\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181459-sfn581er/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181459-sfn581er/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181459-sfn581er/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181459-sfn581er/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181459-sfn581er/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181459-sfn581er/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181459-sfn581er/media/plotly/test_plot_20_1d2f2943.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181459-sfn581er/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181459-sfn581er/media/plotly\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: sfn581er \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181459-sfn581er/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181459-sfn581er/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181459-sfn581er/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181459-sfn581er/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181459-sfn581er/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181459-sfn581er/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: sfn581er\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: gdvkn2kb with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: gdvkn2kb\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/gdvkn2kb\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/gdvkn2kb</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/gdvkn2kb\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmdkdmtuMmtiOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "12.737247206270695\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 0\n",
            "0.5307186335946122\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181512-gdvkn2kb/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181512-gdvkn2kb/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181512-gdvkn2kb/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181512-gdvkn2kb/media/graph/graph_0_summary_b9282d02.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181512-gdvkn2kb/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181512-gdvkn2kb/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181512-gdvkn2kb/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181512-gdvkn2kb/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181512-gdvkn2kb/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.846482746303082\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.5352701144292951\n",
            "The running loss is:\n",
            "8.131057146936655\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.3387940477890273\n",
            "1\n",
            "The running loss is:\n",
            "5.895240487996489\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 3\n",
            "0.24563502033318704\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181512-gdvkn2kb/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181512-gdvkn2kb/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.398755688220263\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 4\n",
            "0.34994815367584425\n",
            "The running loss is:\n",
            "16.694155018776655\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n",
            "0.6955897924490273\n",
            "The running loss is:\n",
            "9.139933694154024\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 6\n",
            "0.380830570589751\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181512-gdvkn2kb/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181512-gdvkn2kb/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.126268687658012\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 7\n",
            "0.21359452865241715\n",
            "2\n",
            "The running loss is:\n",
            "4.945361414458603\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 8\n",
            "0.2060567256024418\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_15PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_15PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181512-gdvkn2kb/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181512-gdvkn2kb/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181512-gdvkn2kb/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  596.721497\n",
            "66  2020-04-22  sub_region  ...             66  611.140503\n",
            "67  2020-04-23  sub_region  ...             67  602.298889\n",
            "68  2020-04-24  sub_region  ...             68  592.272522\n",
            "69  2020-04-25  sub_region  ...             69  590.910889\n",
            "70  2020-04-26  sub_region  ...             70  592.348145\n",
            "71  2020-04-27  sub_region  ...             71  565.893555\n",
            "72  2020-04-28  sub_region  ...             72  597.307617\n",
            "73  2020-04-29  sub_region  ...             73  598.141846\n",
            "74  2020-04-30  sub_region  ...             74  604.241028\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['gdvkn2kb']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181512-gdvkn2kb/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181512-gdvkn2kb/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181512-gdvkn2kb/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181512-gdvkn2kb/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181512-gdvkn2kb/media/plotly/test_plot_18_b0d51432.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181512-gdvkn2kb/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181512-gdvkn2kb/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: gdvkn2kb \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181512-gdvkn2kb/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181512-gdvkn2kb/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181512-gdvkn2kb/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181512-gdvkn2kb/media/plotly/test_plot_all_19_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181512-gdvkn2kb/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181512-gdvkn2kb/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: gdvkn2kb\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: fqkznitg with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: fqkznitg\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/fqkznitg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/fqkznitg</a><br/>\n",
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        {
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/fqkznitg\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmZxa3puaXRnOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "15.76520562171936\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 0\n",
            "0.65688356757164\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181524-fqkznitg/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181524-fqkznitg/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181524-fqkznitg/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181524-fqkznitg/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181524-fqkznitg/media/graph/graph_0_summary_e7bd39af.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181524-fqkznitg/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181524-fqkznitg/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181524-fqkznitg/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181524-fqkznitg/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.477864772081375\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.6449110321700573\n",
            "1\n",
            "The running loss is:\n",
            "12.414291091263294\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.5172621288026372\n",
            "2\n",
            "The running loss is:\n",
            "7.5504014934413135\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 3\n",
            "0.3146000622267214\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181524-fqkznitg/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181524-fqkznitg/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.823520310223103\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 4\n",
            "0.5343133462592959\n",
            "1\n",
            "The running loss is:\n",
            "6.759511549025774\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n",
            "0.2816463145427406\n",
            "The running loss is:\n",
            "6.30719225294888\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 6\n",
            "0.26279967720620334\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181524-fqkznitg/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181524-fqkznitg/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.85306416079402\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 7\n",
            "0.28554434003308415\n",
            "2\n",
            "The running loss is:\n",
            "6.9310750886797905\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 8\n",
            "0.2887947953616579\n",
            "The running loss is:\n",
            "8.455319091677666\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 9\n",
            "0.3523049621532361\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202006_15PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181524-fqkznitg/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181524-fqkznitg/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_15PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['fqkznitg']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  779.131897\n",
            "66  2020-04-22  sub_region  ...             66  792.555298\n",
            "67  2020-04-23  sub_region  ...             67  780.039795\n",
            "68  2020-04-24  sub_region  ...             68  769.559204\n",
            "69  2020-04-25  sub_region  ...             69  768.763855\n",
            "70  2020-04-26  sub_region  ...             70  775.261108\n",
            "71  2020-04-27  sub_region  ...             71  753.575806\n",
            "72  2020-04-28  sub_region  ...             72  784.202087\n",
            "73  2020-04-29  sub_region  ...             73  783.728699\n",
            "74  2020-04-30  sub_region  ...             74  785.233765\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181524-fqkznitg/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181524-fqkznitg/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181524-fqkznitg/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181524-fqkznitg/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181524-fqkznitg/media/plotly/test_plot_20_f7c7c22f.plotly.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181524-fqkznitg/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181524-fqkznitg/media/plotly\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: fqkznitg \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181524-fqkznitg/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181524-fqkznitg/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181524-fqkznitg/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181524-fqkznitg/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181524-fqkznitg/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181524-fqkznitg/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: fqkznitg\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 8svfqbv0 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 8svfqbv0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/8svfqbv0\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/8svfqbv0</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/8svfqbv0\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjhzdmZxYnYwOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "15.728306412696838\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 0\n",
            "0.655346100529035\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181538-8svfqbv0/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181538-8svfqbv0/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181538-8svfqbv0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181538-8svfqbv0/wandb-metadata.json\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.534287229180336\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.5639286345491806\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181538-8svfqbv0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181538-8svfqbv0/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181538-8svfqbv0/media/graph\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "10.27218734100461\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.4280078058751921\n",
            "2\n",
            "The running loss is:\n",
            "5.5147014474496245\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 3\n",
            "0.22977922697706768\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181538-8svfqbv0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181538-8svfqbv0/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.376355066895485\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 4\n",
            "0.3906814611206452\n",
            "1\n",
            "The running loss is:\n",
            "6.485733915120363\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n",
            "0.27023891313001513\n",
            "2\n",
            "The running loss is:\n",
            "7.979923363775015\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 6\n",
            "0.33249680682395893\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181538-8svfqbv0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181538-8svfqbv0/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.143040359020233\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 7\n",
            "0.33929334829250973\n",
            "1\n",
            "The running loss is:\n",
            "5.536451553925872\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 8\n",
            "0.230685481413578\n",
            "2\n",
            "The running loss is:\n",
            "4.550636069383472\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 9\n",
            "0.18960983622431135\n",
            "Data saved to: \n",
            "28_May_202006_15PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181538-8svfqbv0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181538-8svfqbv0/wandb-history.jsonl\n"
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        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_15PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['8svfqbv0']\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  756.924194\n",
            "66  2020-04-22  sub_region  ...             66  788.874878\n",
            "67  2020-04-23  sub_region  ...             67  767.624146\n",
            "68  2020-04-24  sub_region  ...             68  745.193481\n",
            "69  2020-04-25  sub_region  ...             69  751.710815\n",
            "70  2020-04-26  sub_region  ...             70  755.871094\n",
            "71  2020-04-27  sub_region  ...             71  710.098877\n",
            "72  2020-04-28  sub_region  ...             72  772.029785\n",
            "73  2020-04-29  sub_region  ...             73  776.352173\n",
            "74  2020-04-30  sub_region  ...             74  783.972778\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181538-8svfqbv0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181538-8svfqbv0/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 8svfqbv0 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181538-8svfqbv0/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181538-8svfqbv0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181538-8svfqbv0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181538-8svfqbv0/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181538-8svfqbv0/media/plotly/test_plot_20_75431aa1.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181538-8svfqbv0/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181538-8svfqbv0/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181538-8svfqbv0/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 8svfqbv0\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: myhifaua with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: myhifaua\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/myhifaua\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/myhifaua</a><br/>\n",
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/myhifaua\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181550-myhifaua/wandb-metadata.json\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOm15aGlmYXVhOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "12.287862300872803\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 0\n",
            "0.5342548826466436\n",
            "The running loss is:\n",
            "13.926748000085354\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 1\n",
            "0.6055107826124067\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181550-myhifaua/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181550-myhifaua/media/graph/graph_0_summary_0827234d.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181550-myhifaua/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181550-myhifaua/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181550-myhifaua/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181550-myhifaua/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181550-myhifaua/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181550-myhifaua/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.354672580957413\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 2\n",
            "0.4936814165633658\n",
            "2\n",
            "The running loss is:\n",
            "9.702557804062963\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 3\n",
            "0.4218503393070853\n",
            "The running loss is:\n",
            "11.627340912818909\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 4\n",
            "0.5055365614269091\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181550-myhifaua/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181550-myhifaua/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.340536650270224\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 5\n",
            "0.275675506533488\n",
            "2\n",
            "The running loss is:\n",
            "10.078678345307708\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 6\n",
            "0.4382034063177264\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['myhifaua']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.461791813373566\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 7\n",
            "0.3679039918858072\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181550-myhifaua/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181550-myhifaua/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.9823848977684975\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 8\n",
            "0.21662543033776077\n",
            "2\n",
            "The running loss is:\n",
            "3.317102179862559\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 9\n",
            "0.14422183390706778\n",
            "Data saved to: \n",
            "28_May_202006_15PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_15PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181550-myhifaua/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181550-myhifaua/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181550-myhifaua/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  835.124390\n",
            "66  2020-04-22  sub_region  ...             66  840.772278\n",
            "67  2020-04-23  sub_region  ...             67  832.618164\n",
            "68  2020-04-24  sub_region  ...             68  835.653015\n",
            "69  2020-04-25  sub_region  ...             69  821.924072\n",
            "70  2020-04-26  sub_region  ...             70  833.929749\n",
            "71  2020-04-27  sub_region  ...             71  823.226929\n",
            "72  2020-04-28  sub_region  ...             72  831.859680\n",
            "73  2020-04-29  sub_region  ...             73  828.141541\n",
            "74  2020-04-30  sub_region  ...             74  831.716797\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181550-myhifaua/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181550-myhifaua/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181550-myhifaua/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181550-myhifaua/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181550-myhifaua/media/plotly/test_plot_20_7a7a1d5c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181550-myhifaua/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181550-myhifaua/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: myhifaua \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181550-myhifaua/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181550-myhifaua/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181550-myhifaua/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181550-myhifaua/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181550-myhifaua/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181550-myhifaua/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: myhifaua\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: a9pg6ijd with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: a9pg6ijd\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/a9pg6ijd\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/a9pg6ijd</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/a9pg6ijd\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmE5cGc2aWpkOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181607-a9pg6ijd/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.292471200227737\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 0\n",
            "0.534455269575119\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181607-a9pg6ijd/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181607-a9pg6ijd/media/graph/graph_0_summary_7460a360.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181607-a9pg6ijd/wandb-summary.json\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181607-a9pg6ijd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181607-a9pg6ijd/requirements.txt\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181607-a9pg6ijd/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.315681867301464\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 1\n",
            "0.6224209507522376\n",
            "1\n",
            "The running loss is:\n",
            "10.289977289736271\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 2\n",
            "0.44739031694505527\n",
            "2\n",
            "The running loss is:\n",
            "7.302962388843298\n",
            "The number of items in train is: \n",
            "23\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181607-a9pg6ijd/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The loss for epoch 3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181607-a9pg6ijd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0.3175201038627521\n",
            "The running loss is:\n",
            "10.639127224683762\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 4\n",
            "0.462570748899294\n",
            "The running loss is:\n",
            "5.083698073402047\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 5\n",
            "0.2210303510174803\n",
            "1\n",
            "The running loss is:\n",
            "4.266114007681608\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 6\n",
            "0.1854832177252873\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181607-a9pg6ijd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181607-a9pg6ijd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "2\n",
            "The running loss is:\n",
            "3.362240646034479\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 7\n",
            "0.14618437591454256\n",
            "The running loss is:\n",
            "5.346718622371554\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 8\n",
            "0.2324660270596328\n",
            "1\n",
            "The running loss is:\n",
            "3.976900838315487\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 9\n",
            "0.17290873210067334\n",
            "Data saved to: \n",
            "28_May_202006_16PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181607-a9pg6ijd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181607-a9pg6ijd/wandb-history.jsonl\n",
            "INFO:wandb.wandb_agent:Running runs: ['a9pg6ijd']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_16PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181607-a9pg6ijd/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  773.226318\n",
            "66  2020-04-22  sub_region  ...             66  785.933838\n",
            "67  2020-04-23  sub_region  ...             67  776.867065\n",
            "68  2020-04-24  sub_region  ...             68  779.012268\n",
            "69  2020-04-25  sub_region  ...             69  770.271240\n",
            "70  2020-04-26  sub_region  ...             70  779.572510\n",
            "71  2020-04-27  sub_region  ...             71  765.994873\n",
            "72  2020-04-28  sub_region  ...             72  776.860962\n",
            "73  2020-04-29  sub_region  ...             73  774.669739\n",
            "74  2020-04-30  sub_region  ...             74  777.345093\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181607-a9pg6ijd/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181607-a9pg6ijd/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181607-a9pg6ijd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181607-a9pg6ijd/wandb-history.jsonl\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181607-a9pg6ijd/media/plotly/test_plot_20_b982c2b4.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181607-a9pg6ijd/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181607-a9pg6ijd/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: a9pg6ijd \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181607-a9pg6ijd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181607-a9pg6ijd/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181607-a9pg6ijd/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181607-a9pg6ijd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181607-a9pg6ijd/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181607-a9pg6ijd/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: a9pg6ijd\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 4r6vcmds with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 4r6vcmds\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/4r6vcmds\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/4r6vcmds</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/4r6vcmds\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjRyNnZjbWRzOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/media/graph/graph_0_summary_ef8144b2.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.145409000921063\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.4058163600368425\n",
            "The running loss is:\n",
            "25.701497849076986\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "1.0280599139630795\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.272816001903266\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.8109126400761306\n",
            "The running loss is:\n",
            "19.751347083598375\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.790053883343935\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.793059289455414\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.7517223715782165\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.612464647740126\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.664498585909605\n",
            "2\n",
            "The running loss is:\n",
            "9.692850414896384\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.38771401659585536\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['4r6vcmds']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.73813858255744\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.7895255433022976\n",
            "The running loss is:\n",
            "12.20803095260635\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.488321238104254\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.476102620712481\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.4590441048284993\n",
            "2\n",
            "Data saved to: \n",
            "28_May_202006_16PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_16PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  801.515869\n",
            "66  2020-04-22  sub_region  ...             66  801.592773\n",
            "67  2020-04-23  sub_region  ...             67  801.521545\n",
            "68  2020-04-24  sub_region  ...             68  801.597168\n",
            "69  2020-04-25  sub_region  ...             69  801.738037\n",
            "70  2020-04-26  sub_region  ...             70  801.605835\n",
            "71  2020-04-27  sub_region  ...             71  801.837097\n",
            "72  2020-04-28  sub_region  ...             72  801.528931\n",
            "73  2020-04-29  sub_region  ...             73  801.504639\n",
            "74  2020-04-30  sub_region  ...             74  801.555054\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/media/plotly/test_plot_20_fcc89d57.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 4r6vcmds \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181624-4r6vcmds/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 4r6vcmds\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: cy2kokyn with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: cy2kokyn\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/cy2kokyn\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/cy2kokyn</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/cy2kokyn\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmN5Mmtva3luOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/media/graph/graph_0_summary_5bda3603.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.096708084311103\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.40386832337244416\n",
            "The running loss is:\n",
            "25.173049704171717\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "1.0069219881668687\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.567884587217122\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.8227153834886849\n",
            "The running loss is:\n",
            "16.932578061707318\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.6773031224682927\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "11.519542964175344\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.4607817185670137\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.808077305089682\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.3523230922035873\n",
            "The running loss is:\n",
            "11.38830050965771\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.4555320203863084\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['cy2kokyn']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.139500390738249\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.28558001562952995\n",
            "2\n",
            "The running loss is:\n",
            "9.381748400162905\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.3752699360065162\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.521757567301393\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.4208703026920557\n",
            "Data saved to: \n",
            "28_May_202006_16PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_16PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  761.296265\n",
            "66  2020-04-22  sub_region  ...             66  761.453735\n",
            "67  2020-04-23  sub_region  ...             67  761.306885\n",
            "68  2020-04-24  sub_region  ...             68  761.469482\n",
            "69  2020-04-25  sub_region  ...             69  761.735107\n",
            "70  2020-04-26  sub_region  ...             70  761.236694\n",
            "71  2020-04-27  sub_region  ...             71  761.615662\n",
            "72  2020-04-28  sub_region  ...             72  760.781616\n",
            "73  2020-04-29  sub_region  ...             73  760.612366\n",
            "74  2020-04-30  sub_region  ...             74  760.868896\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/media/plotly/test_plot_20_b3c2d6a3.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: cy2kokyn \n",
            "\n"
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          "name": "stdout"
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          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181641-cy2kokyn/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: cy2kokyn\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: p1uy2kwb with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: p1uy2kwb\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/p1uy2kwb\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/p1uy2kwb</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/p1uy2kwb\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnAxdXkya3diOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
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        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181658-p1uy2kwb/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181658-p1uy2kwb/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181658-p1uy2kwb/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181658-p1uy2kwb/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181658-p1uy2kwb/media/graph/graph_0_summary_085426ee.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181658-p1uy2kwb/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181658-p1uy2kwb/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181658-p1uy2kwb/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181658-p1uy2kwb/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.584014743566513\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.8233605897426606\n",
            "The running loss is:\n",
            "17.932957649230957\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.7173183059692383\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181658-p1uy2kwb/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181658-p1uy2kwb/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.548911690013483\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.6219564676005394\n",
            "The running loss is:\n",
            "14.730191620066762\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.5892076648026705\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181658-p1uy2kwb/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181658-p1uy2kwb/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.559959415346384\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.6223983766138553\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181658-p1uy2kwb/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181658-p1uy2kwb/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.396017402410507\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.4558406960964203\n",
            "2\n",
            "The running loss is:\n",
            "9.876246387138963\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.3950498554855585\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_17PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.wandb_agent:Running runs: ['p1uy2kwb']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_17PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181658-p1uy2kwb/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181658-p1uy2kwb/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181658-p1uy2kwb/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  505.433014\n",
            "66  2020-04-22  sub_region  ...             66  504.062500\n",
            "67  2020-04-23  sub_region  ...             67  504.517761\n",
            "68  2020-04-24  sub_region  ...             68  503.535156\n",
            "69  2020-04-25  sub_region  ...             69  506.940369\n",
            "70  2020-04-26  sub_region  ...             70  505.692200\n",
            "71  2020-04-27  sub_region  ...             71  506.551117\n",
            "72  2020-04-28  sub_region  ...             72  507.229309\n",
            "73  2020-04-29  sub_region  ...             73  507.427246\n",
            "74  2020-04-30  sub_region  ...             74  507.579987\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181658-p1uy2kwb/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181658-p1uy2kwb/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181658-p1uy2kwb/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181658-p1uy2kwb/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181658-p1uy2kwb/media/plotly/test_plot_14_c7166984.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181658-p1uy2kwb/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181658-p1uy2kwb/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: p1uy2kwb \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181658-p1uy2kwb/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181658-p1uy2kwb/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181658-p1uy2kwb/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181658-p1uy2kwb/media/plotly/test_plot_all_15_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181658-p1uy2kwb/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181658-p1uy2kwb/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: p1uy2kwb\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: d2rvnib6 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: d2rvnib6\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/d2rvnib6\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/d2rvnib6</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/d2rvnib6\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmQycnZuaWI2OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181715-d2rvnib6/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181715-d2rvnib6/media/graph/graph_0_summary_1aff1725.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181715-d2rvnib6/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181715-d2rvnib6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181715-d2rvnib6/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181715-d2rvnib6/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181715-d2rvnib6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181715-d2rvnib6/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181715-d2rvnib6/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.51875665783882\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.8207502663135529\n",
            "The running loss is:\n",
            "18.745911203324795\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.7498364481329918\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181715-d2rvnib6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181715-d2rvnib6/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.171599298715591\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.5668639719486237\n",
            "2\n",
            "The running loss is:\n",
            "10.58555408520624\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.4234221634082496\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181715-d2rvnib6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181715-d2rvnib6/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.37247646227479\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.6948990584909915\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181715-d2rvnib6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181715-d2rvnib6/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.633864669129252\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.4253545867651701\n",
            "1\n",
            "The running loss is:\n",
            "9.219570185989141\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.36878280743956565\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['d2rvnib6']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181715-d2rvnib6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181715-d2rvnib6/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.460247997194529\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.33840991988778113\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_17PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_17PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181715-d2rvnib6/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181715-d2rvnib6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181715-d2rvnib6/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  684.219727\n",
            "66  2020-04-22  sub_region  ...             66  684.323364\n",
            "67  2020-04-23  sub_region  ...             67  684.080750\n",
            "68  2020-04-24  sub_region  ...             68  683.885254\n",
            "69  2020-04-25  sub_region  ...             69  685.422363\n",
            "70  2020-04-26  sub_region  ...             70  684.204346\n",
            "71  2020-04-27  sub_region  ...             71  685.634094\n",
            "72  2020-04-28  sub_region  ...             72  684.494141\n",
            "73  2020-04-29  sub_region  ...             73  684.385315\n",
            "74  2020-04-30  sub_region  ...             74  684.536133\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181715-d2rvnib6/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181715-d2rvnib6/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181715-d2rvnib6/wandb-history.jsonl\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181715-d2rvnib6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181715-d2rvnib6/media/plotly/test_plot_16_e1926f30.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181715-d2rvnib6/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181715-d2rvnib6/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: d2rvnib6 \n",
            "\n"
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          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181715-d2rvnib6/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181715-d2rvnib6/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181715-d2rvnib6/media/plotly/test_plot_all_17_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181715-d2rvnib6/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181715-d2rvnib6/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: d2rvnib6\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
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          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: gg4ud0e1 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: gg4ud0e1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/gg4ud0e1\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/gg4ud0e1</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/gg4ud0e1\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmdnNHVkMGUxOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
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          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181733-gg4ud0e1/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181733-gg4ud0e1/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181733-gg4ud0e1/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181733-gg4ud0e1/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181733-gg4ud0e1/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181733-gg4ud0e1/wandb-metadata.json\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.772414907813072\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 0\n",
            "0.7821839544922113\n",
            "The running loss is:\n",
            "19.575059436261654\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.8156274765109023\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181733-gg4ud0e1/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181733-gg4ud0e1/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.316046819090843\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.6381686174621185\n",
            "The running loss is:\n",
            "11.947806634008884\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 3\n",
            "0.49782527641703683\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181733-gg4ud0e1/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181733-gg4ud0e1/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.35117731243372\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 4\n",
            "0.43129905468473834\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181733-gg4ud0e1/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181733-gg4ud0e1/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.100783228874207\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n",
            "0.4625326345364253\n",
            "2\n",
            "The running loss is:\n",
            "11.392054236494005\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 6\n",
            "0.4746689265205835\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['gg4ud0e1']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181733-gg4ud0e1/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181733-gg4ud0e1/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.173449165187776\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 7\n",
            "0.215560381882824\n",
            "1\n",
            "The running loss is:\n",
            "9.032942300196737\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 8\n",
            "0.3763725958415307\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181733-gg4ud0e1/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181733-gg4ud0e1/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.382783502340317\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 9\n",
            "0.8076159792641798\n",
            "Data saved to: \n",
            "28_May_202006_17PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_17PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181733-gg4ud0e1/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181733-gg4ud0e1/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181733-gg4ud0e1/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  515.919434\n",
            "66  2020-04-22  sub_region  ...             66  516.543030\n",
            "67  2020-04-23  sub_region  ...             67  515.078186\n",
            "68  2020-04-24  sub_region  ...             68  516.592896\n",
            "69  2020-04-25  sub_region  ...             69  515.011414\n",
            "70  2020-04-26  sub_region  ...             70  514.803894\n",
            "71  2020-04-27  sub_region  ...             71  516.728943\n",
            "72  2020-04-28  sub_region  ...             72  515.620483\n",
            "73  2020-04-29  sub_region  ...             73  515.851013\n",
            "74  2020-04-30  sub_region  ...             74  514.158508\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181733-gg4ud0e1/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181733-gg4ud0e1/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181733-gg4ud0e1/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181733-gg4ud0e1/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181733-gg4ud0e1/media/plotly/test_plot_20_14ad17d9.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181733-gg4ud0e1/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181733-gg4ud0e1/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: gg4ud0e1 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181733-gg4ud0e1/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181733-gg4ud0e1/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181733-gg4ud0e1/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181733-gg4ud0e1/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181733-gg4ud0e1/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181733-gg4ud0e1/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: gg4ud0e1\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 0xnq5rx0 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 0xnq5rx0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/0xnq5rx0\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/0xnq5rx0</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/0xnq5rx0\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjB4bnE1cngwOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181750-0xnq5rx0/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181750-0xnq5rx0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181750-0xnq5rx0/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181750-0xnq5rx0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181750-0xnq5rx0/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181750-0xnq5rx0/media/graph/graph_0_summary_45195203.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181750-0xnq5rx0/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181750-0xnq5rx0/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181750-0xnq5rx0/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.797405660152435\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 0\n",
            "0.7832252358396848\n",
            "The running loss is:\n",
            "19.402401946485043\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.8084334144368768\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181750-0xnq5rx0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181750-0xnq5rx0/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.805835217237473\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.616909800718228\n",
            "The running loss is:\n",
            "9.891540326178074\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 3\n",
            "0.4121475135907531\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181750-0xnq5rx0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181750-0xnq5rx0/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.6078918017446995\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 4\n",
            "0.23366215840602914\n",
            "2\n",
            "The running loss is:\n",
            "4.041457449435256\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181750-0xnq5rx0/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0.16839406039313567\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181750-0xnq5rx0/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_17PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_17PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['0xnq5rx0']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181750-0xnq5rx0/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181750-0xnq5rx0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181750-0xnq5rx0/wandb-summary.json\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  378.001099\n",
            "66  2020-04-22  sub_region  ...             66  379.296204\n",
            "67  2020-04-23  sub_region  ...             67  378.866272\n",
            "68  2020-04-24  sub_region  ...             68  383.898071\n",
            "69  2020-04-25  sub_region  ...             69  369.884155\n",
            "70  2020-04-26  sub_region  ...             70  381.321381\n",
            "71  2020-04-27  sub_region  ...             71  371.926025\n",
            "72  2020-04-28  sub_region  ...             72  378.183258\n",
            "73  2020-04-29  sub_region  ...             73  379.500549\n",
            "74  2020-04-30  sub_region  ...             74  378.237946\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181750-0xnq5rx0/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181750-0xnq5rx0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181750-0xnq5rx0/wandb-summary.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181750-0xnq5rx0/media/plotly/test_plot_12_9498e4f6.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181750-0xnq5rx0/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181750-0xnq5rx0/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 0xnq5rx0 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181750-0xnq5rx0/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181750-0xnq5rx0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181750-0xnq5rx0/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181750-0xnq5rx0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181750-0xnq5rx0/media/plotly/test_plot_all_13_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181750-0xnq5rx0/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 0xnq5rx0\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: juzsqvdl with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: juzsqvdl\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/juzsqvdl\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/juzsqvdl</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/juzsqvdl\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmp1enNxdmRsOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181802-juzsqvdl/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181802-juzsqvdl/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181802-juzsqvdl/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181802-juzsqvdl/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181802-juzsqvdl/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181802-juzsqvdl/media/graph/graph_0_summary_28380503.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181802-juzsqvdl/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181802-juzsqvdl/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.451373293995857\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181802-juzsqvdl/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 0\n",
            "0.8938072205831608\n",
            "The running loss is:\n",
            "18.959119454026222\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.7899633105844259\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181802-juzsqvdl/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181802-juzsqvdl/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.811535373330116\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.7004806405554215\n",
            "The running loss is:\n",
            "15.114799819886684\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 3\n",
            "0.6297833258286119\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181802-juzsqvdl/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181802-juzsqvdl/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.837378360331059\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 4\n",
            "0.5765574316804608\n",
            "2\n",
            "The running loss is:\n",
            "10.938715167343616\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n",
            "0.45577979863931734\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181802-juzsqvdl/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181802-juzsqvdl/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_18PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_18PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['juzsqvdl']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  379.669922\n",
            "66  2020-04-22  sub_region  ...             66  381.567261\n",
            "67  2020-04-23  sub_region  ...             67  381.375244\n",
            "68  2020-04-24  sub_region  ...             68  386.184875\n",
            "69  2020-04-25  sub_region  ...             69  368.961060\n",
            "70  2020-04-26  sub_region  ...             70  383.574829\n",
            "71  2020-04-27  sub_region  ...             71  370.266907\n",
            "72  2020-04-28  sub_region  ...             72  379.182159\n",
            "73  2020-04-29  sub_region  ...             73  380.702667\n",
            "74  2020-04-30  sub_region  ...             74  380.433716\n",
            "\n",
            "[25 rows x 28 columns]"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181802-juzsqvdl/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181802-juzsqvdl/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181802-juzsqvdl/wandb-history.jsonl\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181802-juzsqvdl/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181802-juzsqvdl/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181802-juzsqvdl/wandb-history.jsonl\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181802-juzsqvdl/media/plotly/test_plot_12_ab9062b5.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181802-juzsqvdl/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181802-juzsqvdl/media/plotly\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: juzsqvdl \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181802-juzsqvdl/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181802-juzsqvdl/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181802-juzsqvdl/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181802-juzsqvdl/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181802-juzsqvdl/media/plotly/test_plot_all_13_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181802-juzsqvdl/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: juzsqvdl\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 62jkcm81 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 62jkcm81\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/62jkcm81\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/62jkcm81</a><br/>\n",
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        {
          "output_type": "stream",
          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/62jkcm81\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjYyamtjbTgxOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181817-62jkcm81/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.45643301308155\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 0\n",
            "0.8940180422117313\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181817-62jkcm81/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181817-62jkcm81/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181817-62jkcm81/media/graph/graph_0_summary_10dd7fe2.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181817-62jkcm81/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181817-62jkcm81/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181817-62jkcm81/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181817-62jkcm81/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181817-62jkcm81/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.878746390342712\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.7866144329309464\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181817-62jkcm81/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181817-62jkcm81/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.909177042543888\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.7045490434393287\n",
            "The running loss is:\n",
            "14.992017075419426\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 3\n",
            "0.6246673781424761\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181817-62jkcm81/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181817-62jkcm81/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.057133480906487\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 4\n",
            "0.5440472283711036\n",
            "2\n",
            "The running loss is:\n",
            "7.168162252753973\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181817-62jkcm81/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181817-62jkcm81/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0.2986734271980822\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_18PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_18PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['62jkcm81']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181817-62jkcm81/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181817-62jkcm81/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181817-62jkcm81/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  381.720642\n",
            "66  2020-04-22  sub_region  ...             66  383.487915\n",
            "67  2020-04-23  sub_region  ...             67  382.618774\n",
            "68  2020-04-24  sub_region  ...             68  386.354279\n",
            "69  2020-04-25  sub_region  ...             69  371.824524\n",
            "70  2020-04-26  sub_region  ...             70  384.303436\n",
            "71  2020-04-27  sub_region  ...             71  373.255554\n",
            "72  2020-04-28  sub_region  ...             72  381.710022\n",
            "73  2020-04-29  sub_region  ...             73  383.322937\n",
            "74  2020-04-30  sub_region  ...             74  382.421906\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181817-62jkcm81/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181817-62jkcm81/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181817-62jkcm81/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181817-62jkcm81/wandb-summary.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181817-62jkcm81/media/plotly/test_plot_12_d4e101e3.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181817-62jkcm81/media\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181817-62jkcm81/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 62jkcm81 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181817-62jkcm81/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181817-62jkcm81/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181817-62jkcm81/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181817-62jkcm81/media/plotly/test_plot_all_13_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181817-62jkcm81/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181817-62jkcm81/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 62jkcm81\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: qzc31mty with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: qzc31mty\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/qzc31mty\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/qzc31mty</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/qzc31mty\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnF6YzMxbXR5OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181828-qzc31mty/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181828-qzc31mty/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "17.729351297020912\n",
            "The number of items in train is: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181828-qzc31mty/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "23\n",
            "The loss for epoch 0\n",
            "0.7708413607400396\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181828-qzc31mty/media/graph/graph_0_summary_59caac40.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181828-qzc31mty/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181828-qzc31mty/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181828-qzc31mty/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181828-qzc31mty/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181828-qzc31mty/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.55451713502407\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 1\n",
            "0.7632398754358292\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181828-qzc31mty/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181828-qzc31mty/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.769226357340813\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 2\n",
            "0.5986620155365571\n",
            "1\n",
            "The running loss is:\n",
            "10.689910009503365\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 3\n",
            "0.46477869606536365\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181828-qzc31mty/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181828-qzc31mty/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.719441205263138\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 4\n",
            "0.4660626610983973\n",
            "The running loss is:\n",
            "11.102143481373787\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 5\n",
            "0.48270189049451245\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181828-qzc31mty/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181828-qzc31mty/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.552160829305649\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 6\n",
            "0.3718330795350282\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['qzc31mty']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.911982571706176\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 7\n",
            "0.3874775031176598\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181828-qzc31mty/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181828-qzc31mty/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.839222326874733\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 8\n",
            "0.4712705359510753\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181828-qzc31mty/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.471042025834322\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181828-qzc31mty/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 9\n",
            "0.28134965329714445\n",
            "Data saved to: \n",
            "28_May_202006_18PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_18PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181828-qzc31mty/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181828-qzc31mty/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181828-qzc31mty/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  682.042175\n",
            "66  2020-04-22  sub_region  ...             66  684.554626\n",
            "67  2020-04-23  sub_region  ...             67  676.001160\n",
            "68  2020-04-24  sub_region  ...             68  688.336792\n",
            "69  2020-04-25  sub_region  ...             69  666.439087\n",
            "70  2020-04-26  sub_region  ...             70  671.417664\n",
            "71  2020-04-27  sub_region  ...             71  689.934692\n",
            "72  2020-04-28  sub_region  ...             72  674.941956\n",
            "73  2020-04-29  sub_region  ...             73  665.878967\n",
            "74  2020-04-30  sub_region  ...             74  658.025818\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181828-qzc31mty/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181828-qzc31mty/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181828-qzc31mty/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181828-qzc31mty/media/plotly/test_plot_20_790f76c3.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181828-qzc31mty/media\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181828-qzc31mty/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: qzc31mty \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181828-qzc31mty/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181828-qzc31mty/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181828-qzc31mty/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181828-qzc31mty/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181828-qzc31mty/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181828-qzc31mty/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: qzc31mty\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: hs2tp4rm with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: hs2tp4rm\n"
          ],
          "name": "stdout"
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          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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          "name": "stderr"
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        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/hs2tp4rm\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/hs2tp4rm</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/hs2tp4rm\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmhzMnRwNHJtOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181846-hs2tp4rm/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.749317228794098\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 0\n",
            "0.7717094447301782\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181846-hs2tp4rm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181846-hs2tp4rm/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181846-hs2tp4rm/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181846-hs2tp4rm/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181846-hs2tp4rm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181846-hs2tp4rm/media/graph/graph_0_summary_fc1378dd.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181846-hs2tp4rm/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181846-hs2tp4rm/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.551174700260162\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 1\n",
            "0.7630945521852245\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181846-hs2tp4rm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181846-hs2tp4rm/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.54475101083517\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 2\n",
            "0.5889022178623987\n",
            "1\n",
            "The running loss is:\n",
            "10.344177260994911\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 3\n",
            "0.44974683743456134\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181846-hs2tp4rm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181846-hs2tp4rm/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.028607338666916\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 4\n",
            "0.39254814515943115\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_18PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_18PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181846-hs2tp4rm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181846-hs2tp4rm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181846-hs2tp4rm/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  391.728210\n",
            "66  2020-04-22  sub_region  ...             66  392.369507\n",
            "67  2020-04-23  sub_region  ...             67  392.830872\n",
            "68  2020-04-24  sub_region  ...             68  397.064850\n",
            "69  2020-04-25  sub_region  ...             69  385.320221\n",
            "70  2020-04-26  sub_region  ...             70  393.841858\n",
            "71  2020-04-27  sub_region  ...             71  388.418610\n",
            "72  2020-04-28  sub_region  ...             72  390.187714\n",
            "73  2020-04-29  sub_region  ...             73  390.757416\n",
            "74  2020-04-30  sub_region  ...             74  391.191467\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['hs2tp4rm']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181846-hs2tp4rm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181846-hs2tp4rm/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181846-hs2tp4rm/wandb-history.jsonl\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181846-hs2tp4rm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181846-hs2tp4rm/media/plotly/test_plot_10_ca77e0e0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181846-hs2tp4rm/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181846-hs2tp4rm/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: hs2tp4rm \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181846-hs2tp4rm/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181846-hs2tp4rm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181846-hs2tp4rm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181846-hs2tp4rm/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181846-hs2tp4rm/media/plotly/test_plot_all_11_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181846-hs2tp4rm/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: hs2tp4rm\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: crrevwn9 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: crrevwn9\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/crrevwn9\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/crrevwn9</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/crrevwn9\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmNycmV2d245OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/media/graph/graph_0_summary_282b85a8.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.765136603731662\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.4306054641492665\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "26.137935783714056\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "1.0455174313485622\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.011536345118657\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.8804614538047463\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.903898189775646\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.7561559275910258\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['crrevwn9']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.865029871463776\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.754601194858551\n",
            "The running loss is:\n",
            "19.70023591836798\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.7880094367347192\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "18.773742999881506\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.7509497199952603\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.47792062163353\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.7791168248653412\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.640535548329353\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.7856214219331741\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.046687852591276\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.7618675141036511\n",
            "Data saved to: \n",
            "28_May_202006_19PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_19PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  496.016388\n",
            "66  2020-04-22  sub_region  ...             66  496.046783\n",
            "67  2020-04-23  sub_region  ...             67  495.631775\n",
            "68  2020-04-24  sub_region  ...             68  495.853607\n",
            "69  2020-04-25  sub_region  ...             69  496.806030\n",
            "70  2020-04-26  sub_region  ...             70  495.802399\n",
            "71  2020-04-27  sub_region  ...             71  497.801544\n",
            "72  2020-04-28  sub_region  ...             72  496.134644\n",
            "73  2020-04-29  sub_region  ...             73  495.858582\n",
            "74  2020-04-30  sub_region  ...             74  495.841339\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/media/plotly\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/wandb-summary.json\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/media\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
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            "wandb: Agent Finished Run: crrevwn9 \n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/wandb-summary.json\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181859-crrevwn9/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: crrevwn9\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
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            "wandb: Agent Starting Run: 73l0xd2r with config:\n",
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            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 73l0xd2r\n"
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          "name": "stdout"
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          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/73l0xd2r\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/73l0xd2r</a><br/>\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/73l0xd2r\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
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          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
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          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181922-73l0xd2r/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181922-73l0xd2r/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181922-73l0xd2r/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181922-73l0xd2r/media/graph/graph_0_summary_eaf0769d.graph.json\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181922-73l0xd2r/wandb-summary.json\n",
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          ],
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        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.082796600530855\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.44331186402123424\n"
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          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181922-73l0xd2r/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181922-73l0xd2r/wandb-summary.json\n"
          ],
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        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "26.767839308828115\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "1.0707135723531247\n"
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        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181922-73l0xd2r/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181922-73l0xd2r/wandb-summary.json\n"
          ],
          "name": "stderr"
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          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.91911711357534\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.7967646845430135\n"
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          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181922-73l0xd2r/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181922-73l0xd2r/wandb-summary.json\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.481270607560873\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.7392508243024349\n",
            "1\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['73l0xd2r']\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.93701122701168\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.7574804490804672\n"
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          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181922-73l0xd2r/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181922-73l0xd2r/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.590259796008468\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.7836103918403388\n"
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          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181922-73l0xd2r/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181922-73l0xd2r/wandb-summary.json\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.897615873254836\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.7559046349301934\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181922-73l0xd2r/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181922-73l0xd2r/wandb-summary.json\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.6400615721941\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.785602462887764\n"
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          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181922-73l0xd2r/wandb-summary.json\n"
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          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.225644499063492\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.7690257799625396\n",
            "1\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181922-73l0xd2r/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.104803455993533\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.7641921382397413\n",
            "2\n",
            "Data saved to: \n",
            "28_May_202006_19PM.json\n"
          ],
          "name": "stdout"
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          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
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        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_19PM_model.pth\n"
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          "name": "stdout"
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          "output_type": "stream",
          "text": [
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        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  495.774384\n",
            "66  2020-04-22  sub_region  ...             66  495.760284\n",
            "67  2020-04-23  sub_region  ...             67  495.068512\n",
            "68  2020-04-24  sub_region  ...             68  496.070099\n",
            "69  2020-04-25  sub_region  ...             69  497.589020\n",
            "70  2020-04-26  sub_region  ...             70  495.803619\n",
            "71  2020-04-27  sub_region  ...             71  500.093933\n",
            "72  2020-04-28  sub_region  ...             72  495.899109\n",
            "73  2020-04-29  sub_region  ...             73  495.294128\n",
            "74  2020-04-30  sub_region  ...             74  495.290405\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181922-73l0xd2r/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181922-73l0xd2r/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181922-73l0xd2r/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181922-73l0xd2r/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181922-73l0xd2r/media/plotly/test_plot_20_b4b150f6.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181922-73l0xd2r/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181922-73l0xd2r/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 73l0xd2r \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181922-73l0xd2r/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181922-73l0xd2r/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181922-73l0xd2r/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181922-73l0xd2r/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181922-73l0xd2r/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181922-73l0xd2r/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 73l0xd2r\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: e0266c8z with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: e0266c8z\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/e0266c8z\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/e0266c8z</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/e0266c8z\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmUwMjY2Yzh6OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/media/graph/graph_0_summary_c2b33b0b.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.70665431022644\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.9082661724090576\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.07255493104458\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.8029021972417831\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.82785291969776\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.7931141167879104\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.11555242538452\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.8446220970153808\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['e0266c8z']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.238652385771275\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.769546095430851\n",
            "1\n",
            "The running loss is:\n",
            "19.169510439038277\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.766780417561531\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.454397417604923\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.778175896704197\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_19PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_19PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  386.295502\n",
            "66  2020-04-22  sub_region  ...             66  386.368958\n",
            "67  2020-04-23  sub_region  ...             67  386.170776\n",
            "68  2020-04-24  sub_region  ...             68  386.041290\n",
            "69  2020-04-25  sub_region  ...             69  386.396393\n",
            "70  2020-04-26  sub_region  ...             70  386.061401\n",
            "71  2020-04-27  sub_region  ...             71  386.280853\n",
            "72  2020-04-28  sub_region  ...             72  386.269623\n",
            "73  2020-04-29  sub_region  ...             73  386.305389\n",
            "74  2020-04-30  sub_region  ...             74  386.221985\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/media/plotly/test_plot_14_416120dd.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: e0266c8z \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/media/plotly/test_plot_all_15_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_181945-e0266c8z/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: e0266c8z\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 5bgwtdhu with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 5bgwtdhu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/5bgwtdhu\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/5bgwtdhu</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/5bgwtdhu\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjViZ3d0ZGh1OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182002-5bgwtdhu/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182002-5bgwtdhu/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182002-5bgwtdhu/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182002-5bgwtdhu/media/graph/graph_0_summary_d588c064.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182002-5bgwtdhu/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182002-5bgwtdhu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182002-5bgwtdhu/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182002-5bgwtdhu/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182002-5bgwtdhu/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.725555673241615\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.9090222269296646\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182002-5bgwtdhu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182002-5bgwtdhu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.579730972647667\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.8231892389059067\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182002-5bgwtdhu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182002-5bgwtdhu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.793140269815922\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.7917256107926369\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182002-5bgwtdhu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182002-5bgwtdhu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.604701928794384\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.8241880771517753\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['5bgwtdhu']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.242981515824795\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.7697192606329918\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182002-5bgwtdhu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182002-5bgwtdhu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "19.15913762152195\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.766365504860878\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182002-5bgwtdhu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182002-5bgwtdhu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.610484287142754\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.7844193714857102\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_20PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182002-5bgwtdhu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182002-5bgwtdhu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_20PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  408.607574\n",
            "66  2020-04-22  sub_region  ...             66  408.763275\n",
            "67  2020-04-23  sub_region  ...             67  408.592804\n",
            "68  2020-04-24  sub_region  ...             68  408.707031\n",
            "69  2020-04-25  sub_region  ...             69  408.915588\n",
            "70  2020-04-26  sub_region  ...             70  408.787262\n",
            "71  2020-04-27  sub_region  ...             71  409.198151\n",
            "72  2020-04-28  sub_region  ...             72  408.587250\n",
            "73  2020-04-29  sub_region  ...             73  408.494568\n",
            "74  2020-04-30  sub_region  ...             74  408.603363\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182002-5bgwtdhu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182002-5bgwtdhu/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 5bgwtdhu \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182002-5bgwtdhu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182002-5bgwtdhu/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182002-5bgwtdhu/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182002-5bgwtdhu/media/plotly/test_plot_14_755375b7.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182002-5bgwtdhu/media/plotly/test_plot_all_15_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182002-5bgwtdhu/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182002-5bgwtdhu/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182002-5bgwtdhu/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 5bgwtdhu\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: m0sfcpsb with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: m0sfcpsb\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/m0sfcpsb\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/m0sfcpsb</a><br/>\n",
              "            "
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            "DEBUG:wandb.meta:probe for git information\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/m0sfcpsb\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOm0wc2ZjcHNiOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/config.yaml\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/wandb-metadata.json\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/requirements.txt\n",
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          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.80333824455738\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 0\n",
            "0.8251390935232242\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.39603229612112\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.93316801233838\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.272497054189444\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.5946873772578934\n",
            "1\n",
            "The running loss is:\n",
            "14.552330110222101\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 3\n",
            "0.6063470879259208\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/wandb-summary.json\n",
            "INFO:wandb.wandb_agent:Running runs: ['m0sfcpsb']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.988487225025892\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 4\n",
            "0.5828536343760788\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.61138915270567\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n",
            "0.6088078813627362\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.06570915877819\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 6\n",
            "0.5444045482824246\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.021730467677116\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 7\n",
            "0.41757210281987983\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.953084118664265\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 8\n",
            "0.49804517161101103\n",
            "1\n",
            "The running loss is:\n",
            "10.495671391487122\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 9\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0.4373196413119634\n",
            "2\n",
            "Data saved to: \n",
            "28_May_202006_20PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_20PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  705.102661\n",
            "66  2020-04-22  sub_region  ...             66  705.103027\n",
            "67  2020-04-23  sub_region  ...             67  705.096924\n",
            "68  2020-04-24  sub_region  ...             68  705.107422\n",
            "69  2020-04-25  sub_region  ...             69  705.110229\n",
            "70  2020-04-26  sub_region  ...             70  705.101257\n",
            "71  2020-04-27  sub_region  ...             71  705.131958\n",
            "72  2020-04-28  sub_region  ...             72  705.105957\n",
            "73  2020-04-29  sub_region  ...             73  705.100586\n",
            "74  2020-04-30  sub_region  ...             74  705.097656\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/wandb-summary.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/media/plotly/test_plot_20_2b3d8713.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: m0sfcpsb \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182019-m0sfcpsb/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: m0sfcpsb\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: b4jmjakq with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: b4jmjakq\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/b4jmjakq\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/b4jmjakq</a><br/>\n",
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          "metadata": {
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        {
          "output_type": "stream",
          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/b4jmjakq\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmI0am1qYWtxOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/media/graph/graph_0_summary_4978548b.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.914355665445328\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 0\n",
            "0.8297648193935553\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.440757513046265\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.9350315630435944\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.276884399354458\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.5948701833064357\n",
            "1\n",
            "The running loss is:\n",
            "14.543040201067924\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 3\n",
            "0.6059600083778302\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/wandb-summary.json\n",
            "INFO:wandb.wandb_agent:Running runs: ['b4jmjakq']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.132904570549726\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 4\n",
            "0.5888710237729052\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.135652396827936\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n",
            "0.5473188498678306\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.404533512890339\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 6\n",
            "0.5168555630370975\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.101209737360477\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 7\n",
            "0.5042170723900199\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.456808343529701\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 8\n",
            "0.6023670143137375\n",
            "1\n",
            "The running loss is:\n",
            "8.79074577242136\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 9\n",
            "0.36628107385089\n",
            "2\n",
            "Data saved to: \n",
            "28_May_202006_20PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_20PM_model.pth\n",
            "interpolate should be below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  748.224915\n",
            "66  2020-04-22  sub_region  ...             66  748.223572\n",
            "67  2020-04-23  sub_region  ...             67  748.228333\n",
            "68  2020-04-24  sub_region  ...             68  748.199829\n",
            "69  2020-04-25  sub_region  ...             69  748.202820\n",
            "70  2020-04-26  sub_region  ...             70  748.207642\n",
            "71  2020-04-27  sub_region  ...             71  748.156372\n",
            "72  2020-04-28  sub_region  ...             72  748.215271\n",
            "73  2020-04-29  sub_region  ...             73  748.225708\n",
            "74  2020-04-30  sub_region  ...             74  748.218994\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/media/plotly/test_plot_20_62c51e74.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/media\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: b4jmjakq \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182042-b4jmjakq/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: b4jmjakq\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Network error resolved after 0:00:15.588386, resuming normal operation.\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: zlpt6bze with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: zlpt6bze\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/zlpt6bze\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/zlpt6bze</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/zlpt6bze\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnpscHQ2YnplOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182118-zlpt6bze/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182118-zlpt6bze/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182118-zlpt6bze/media/graph/graph_0_summary_3777feef.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182118-zlpt6bze/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182118-zlpt6bze/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182118-zlpt6bze/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182118-zlpt6bze/requirements.txt\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182118-zlpt6bze/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.53992810845375\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 0\n",
            "0.939163671185573\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182118-zlpt6bze/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182118-zlpt6bze/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.258116833865643\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.8024215347444018\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182118-zlpt6bze/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182118-zlpt6bze/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.27618619799614\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.6781744249165058\n",
            "The running loss is:\n",
            "15.538168050348759\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 3\n",
            "0.6474236687645316\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182118-zlpt6bze/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182118-zlpt6bze/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.75120346993208\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 4\n",
            "0.6563001445805033\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['zlpt6bze']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182118-zlpt6bze/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182118-zlpt6bze/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.720529459416866\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n",
            "0.6550220608090361\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182118-zlpt6bze/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182118-zlpt6bze/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.435486644506454\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 6\n",
            "0.6431452768544356\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182118-zlpt6bze/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182118-zlpt6bze/wandb-summary.json\n"
          ],
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        },
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          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.386884167790413\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 7\n",
            "0.6411201736579338\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182118-zlpt6bze/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182118-zlpt6bze/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.28485395014286\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 8\n",
            "0.6368689145892859\n",
            "The running loss is:\n",
            "15.743594877421856\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 9\n",
            "0.6559831198925773\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202006_21PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182118-zlpt6bze/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182118-zlpt6bze/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_21PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  400.785828\n",
            "66  2020-04-22  sub_region  ...             66  401.776733\n",
            "67  2020-04-23  sub_region  ...             67  401.619812\n",
            "68  2020-04-24  sub_region  ...             68  403.111847\n",
            "69  2020-04-25  sub_region  ...             69  400.459259\n",
            "70  2020-04-26  sub_region  ...             70  402.953522\n",
            "71  2020-04-27  sub_region  ...             71  400.775208\n",
            "72  2020-04-28  sub_region  ...             72  400.355286\n",
            "73  2020-04-29  sub_region  ...             73  400.579224\n",
            "74  2020-04-30  sub_region  ...             74  401.702911\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182118-zlpt6bze/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182118-zlpt6bze/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: zlpt6bze \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182118-zlpt6bze/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182118-zlpt6bze/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182118-zlpt6bze/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182118-zlpt6bze/media/plotly/test_plot_20_42d1f4aa.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182118-zlpt6bze/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182118-zlpt6bze/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182118-zlpt6bze/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182118-zlpt6bze/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: zlpt6bze\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: gmmqn5n2 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: gmmqn5n2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/gmmqn5n2\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/gmmqn5n2</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/gmmqn5n2\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmdtbXFuNW4yOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/media/graph/graph_0_summary_046522db.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.067732363939285\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 0\n",
            "0.8290318419104037\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.485696509480476\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 1\n",
            "0.8037259351948033\n",
            "The running loss is:\n",
            "13.346832662820816\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 2\n",
            "0.5802970722965572\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.152954459190369\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 3\n",
            "0.5718675851821899\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.9220210313797\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 4\n",
            "0.5183487404947695\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['gmmqn5n2']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.841944128274918\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 5\n",
            "0.38443235340325727\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.500840276479721\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 6\n",
            "0.5000365337599879\n",
            "The running loss is:\n",
            "7.89447258785367\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 7\n",
            "0.3432379386023335\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.601438160985708\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 8\n",
            "0.3304973113472047\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.348183668218553\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 9\n",
            "0.18905146383558927\n",
            "Data saved to: \n",
            "28_May_202006_21PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_21PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  752.961365\n",
            "66  2020-04-22  sub_region  ...             66  752.971558\n",
            "67  2020-04-23  sub_region  ...             67  752.971436\n",
            "68  2020-04-24  sub_region  ...             68  752.982910\n",
            "69  2020-04-25  sub_region  ...             69  753.031128\n",
            "70  2020-04-26  sub_region  ...             70  753.011536\n",
            "71  2020-04-27  sub_region  ...             71  753.077209\n",
            "72  2020-04-28  sub_region  ...             72  752.963745\n",
            "73  2020-04-29  sub_region  ...             73  752.952332\n",
            "74  2020-04-30  sub_region  ...             74  752.973022\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: gmmqn5n2 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/media/plotly/test_plot_20_b60ea359.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182138-gmmqn5n2/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: gmmqn5n2\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: ltljjgyz with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: ltljjgyz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/ltljjgyz\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/ltljjgyz</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/ltljjgyz\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmx0bGpqZ3l6OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/media/graph/graph_0_summary_e40e6689.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.090508729219437\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 0\n",
            "0.8300221186617146\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.449677228927612\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 1\n",
            "0.8021598795185918\n",
            "The running loss is:\n",
            "13.39623960852623\n",
            "The number of items in train is: \n",
            "23\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The loss for epoch 2\n",
            "0.5824452003707057\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.160676665604115\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 3\n",
            "0.5722033332871355\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.167915090918541\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 4\n",
            "0.5290397865616757\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['ltljjgyz']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.957148090004921\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 5\n",
            "0.38944122130456177\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.491320692002773\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 6\n",
            "0.28223133443490317\n",
            "2\n",
            "The running loss is:\n",
            "6.585609849542379\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 7\n",
            "0.28633086302358174\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.37033998966217\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 8\n",
            "0.8856669560722683\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.08682893961668\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 9\n",
            "0.5689925625920296\n",
            "Data saved to: \n",
            "28_May_202006_22PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_22PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  439.419891\n",
            "66  2020-04-22  sub_region  ...             66  439.420502\n",
            "67  2020-04-23  sub_region  ...             67  439.421204\n",
            "68  2020-04-24  sub_region  ...             68  439.428345\n",
            "69  2020-04-25  sub_region  ...             69  439.419373\n",
            "70  2020-04-26  sub_region  ...             70  439.426117\n",
            "71  2020-04-27  sub_region  ...             71  439.426697\n",
            "72  2020-04-28  sub_region  ...             72  439.417877\n",
            "73  2020-04-29  sub_region  ...             73  439.417023\n",
            "74  2020-04-30  sub_region  ...             74  439.419281\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/media/plotly/test_plot_20_2f23849f.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: ltljjgyz \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182155-ltljjgyz/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: ltljjgyz\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: hh6xz8uc with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: hh6xz8uc\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/hh6xz8uc\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/hh6xz8uc</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/hh6xz8uc\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmhoNnh6OHVjOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "13.29942782886792\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.5319771131547167\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182215-hh6xz8uc/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182215-hh6xz8uc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182215-hh6xz8uc/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182215-hh6xz8uc/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182215-hh6xz8uc/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182215-hh6xz8uc/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182215-hh6xz8uc/media/graph/graph_0_summary_f49bd55b.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182215-hh6xz8uc/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182215-hh6xz8uc/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.958505789749324\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.638340231589973\n",
            "The running loss is:\n",
            "9.610189565457404\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.38440758261829616\n",
            "The running loss is:\n",
            "8.331762389920186\n",
            "The number of items in train is: \n",
            "25\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182215-hh6xz8uc/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182215-hh6xz8uc/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The loss for epoch 3\n",
            "0.33327049559680744\n",
            "1\n",
            "The running loss is:\n",
            "6.60916328427993\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.2643665313711972\n",
            "2\n",
            "The running loss is:\n",
            "6.482465127395699\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.25929860509582797\n",
            "The running loss is:\n",
            "7.1515498894732445\n",
            "The number of items in train is: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182215-hh6xz8uc/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "25\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182215-hh6xz8uc/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The loss for epoch 6\n",
            "0.2860619955789298\n",
            "The running loss is:\n",
            "6.881212463777047\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.27524849855108185\n",
            "The running loss is:\n",
            "7.631635777652264\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.30526543110609056\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182215-hh6xz8uc/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182215-hh6xz8uc/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.2557811347651295\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.2502312453906052\n",
            "Data saved to: \n",
            "28_May_202006_22PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_22PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['hh6xz8uc']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182215-hh6xz8uc/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182215-hh6xz8uc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182215-hh6xz8uc/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  801.962341\n",
            "66  2020-04-22  sub_region  ...             66  824.838013\n",
            "67  2020-04-23  sub_region  ...             67  775.098450\n",
            "68  2020-04-24  sub_region  ...             68  820.728394\n",
            "69  2020-04-25  sub_region  ...             69  858.230591\n",
            "70  2020-04-26  sub_region  ...             70  790.655151\n",
            "71  2020-04-27  sub_region  ...             71  957.766296\n",
            "72  2020-04-28  sub_region  ...             72  793.469360\n",
            "73  2020-04-29  sub_region  ...             73  769.994263\n",
            "74  2020-04-30  sub_region  ...             74  771.788574\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182215-hh6xz8uc/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182215-hh6xz8uc/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182215-hh6xz8uc/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182215-hh6xz8uc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182215-hh6xz8uc/media/plotly/test_plot_20_302bf663.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182215-hh6xz8uc/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182215-hh6xz8uc/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: hh6xz8uc \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182215-hh6xz8uc/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182215-hh6xz8uc/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182215-hh6xz8uc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182215-hh6xz8uc/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182215-hh6xz8uc/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182215-hh6xz8uc/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: hh6xz8uc\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: f5uowdv7 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: f5uowdv7\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/f5uowdv7\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/f5uowdv7</a><br/>\n",
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        {
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/f5uowdv7\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmY1dW93ZHY3OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "13.510150267975405\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.5404060107190162\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182233-f5uowdv7/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182233-f5uowdv7/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182233-f5uowdv7/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182233-f5uowdv7/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182233-f5uowdv7/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182233-f5uowdv7/media/graph/graph_0_summary_146b15c3.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182233-f5uowdv7/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182233-f5uowdv7/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182233-f5uowdv7/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.415556019172072\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.6566222407668829\n",
            "1\n",
            "The running loss is:\n",
            "8.791841979138553\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.35167367916554215\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182233-f5uowdv7/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182233-f5uowdv7/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "9.764055475010537\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.3905622190004215\n",
            "The running loss is:\n",
            "7.065425713779405\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.2826170285511762\n",
            "1\n",
            "The running loss is:\n",
            "6.248815768616623\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.24995263074466492\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182233-f5uowdv7/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182233-f5uowdv7/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.674299073871225\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.266971962954849\n",
            "1\n",
            "The running loss is:\n",
            "7.030096196685918\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.2812038478674367\n",
            "The running loss is:\n",
            "8.11010273359716\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.32440410934388636\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182233-f5uowdv7/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182233-f5uowdv7/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.296146783512086\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.33184587134048343\n",
            "Data saved to: \n",
            "28_May_202006_22PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_22PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['f5uowdv7']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182233-f5uowdv7/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182233-f5uowdv7/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182233-f5uowdv7/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  725.814941\n",
            "66  2020-04-22  sub_region  ...             66  739.527832\n",
            "67  2020-04-23  sub_region  ...             67  716.998169\n",
            "68  2020-04-24  sub_region  ...             68  738.462158\n",
            "69  2020-04-25  sub_region  ...             69  756.830688\n",
            "70  2020-04-26  sub_region  ...             70  728.941650\n",
            "71  2020-04-27  sub_region  ...             71  807.169800\n",
            "72  2020-04-28  sub_region  ...             72  725.725586\n",
            "73  2020-04-29  sub_region  ...             73  714.828308\n",
            "74  2020-04-30  sub_region  ...             74  716.417358\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182233-f5uowdv7/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182233-f5uowdv7/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182233-f5uowdv7/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182233-f5uowdv7/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182233-f5uowdv7/media/plotly/test_plot_20_f6cc8bc8.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182233-f5uowdv7/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182233-f5uowdv7/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: f5uowdv7 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182233-f5uowdv7/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182233-f5uowdv7/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182233-f5uowdv7/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182233-f5uowdv7/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182233-f5uowdv7/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182233-f5uowdv7/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: f5uowdv7\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: o9ibe0l0 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: o9ibe0l0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/o9ibe0l0\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/o9ibe0l0</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/o9ibe0l0\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOm85aWJlMGwwOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "19.00246888399124\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.7600987553596497\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182245-o9ibe0l0/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182245-o9ibe0l0/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182245-o9ibe0l0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182245-o9ibe0l0/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182245-o9ibe0l0/wandb-summary.json\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.036757367663085\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.6414702947065234\n",
            "1\n",
            "The running loss is:\n",
            "10.140504654031247\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.4056201861612499\n",
            "The running loss is:\n",
            "11.365801957435906\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.45463207829743624\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182245-o9ibe0l0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182245-o9ibe0l0/wandb-summary.json\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "15.48135045915842\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.6192540183663369\n",
            "2\n",
            "The running loss is:\n",
            "8.226837230846286\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.32907348923385144\n",
            "The running loss is:\n",
            "7.584667568095028\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.30338670272380114\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182245-o9ibe0l0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182245-o9ibe0l0/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "8.201896103098989\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.3280758441239595\n",
            "2\n",
            "The running loss is:\n",
            "8.102215321734548\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.3240886128693819\n",
            "The running loss is:\n",
            "8.849566355347633\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.35398265421390535\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182245-o9ibe0l0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182245-o9ibe0l0/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "Data saved to: \n",
            "28_May_202006_22PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_22PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['o9ibe0l0']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182245-o9ibe0l0/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182245-o9ibe0l0/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182245-o9ibe0l0/wandb-summary.json\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  721.418884\n",
            "66  2020-04-22  sub_region  ...             66  729.418823\n",
            "67  2020-04-23  sub_region  ...             67  704.071716\n",
            "68  2020-04-24  sub_region  ...             68  698.017456\n",
            "69  2020-04-25  sub_region  ...             69  721.563843\n",
            "70  2020-04-26  sub_region  ...             70  702.487244\n",
            "71  2020-04-27  sub_region  ...             71  729.263550\n",
            "72  2020-04-28  sub_region  ...             72  726.579834\n",
            "73  2020-04-29  sub_region  ...             73  722.222656\n",
            "74  2020-04-30  sub_region  ...             74  718.942139\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182245-o9ibe0l0/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182245-o9ibe0l0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182245-o9ibe0l0/wandb-summary.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182245-o9ibe0l0/media/plotly/test_plot_20_359c7a7b.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182245-o9ibe0l0/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182245-o9ibe0l0/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: o9ibe0l0 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182245-o9ibe0l0/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182245-o9ibe0l0/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182245-o9ibe0l0/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182245-o9ibe0l0/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182245-o9ibe0l0/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182245-o9ibe0l0/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: o9ibe0l0\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: i4hyn3mi with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: i4hyn3mi\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/i4hyn3mi\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/i4hyn3mi</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/i4hyn3mi\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmk0aHluM21pOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "18.724818095564842\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.7489927238225937\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182258-i4hyn3mi/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182258-i4hyn3mi/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182258-i4hyn3mi/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182258-i4hyn3mi/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182258-i4hyn3mi/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182258-i4hyn3mi/media/graph/graph_0_summary_a1e7384c.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182258-i4hyn3mi/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182258-i4hyn3mi/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182258-i4hyn3mi/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.727116253226995\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.6290846501290798\n",
            "1\n",
            "The running loss is:\n",
            "8.56138514901977\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.3424554059607908\n",
            "The running loss is:\n",
            "10.345093304291368\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.4138037321716547\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182258-i4hyn3mi/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182258-i4hyn3mi/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "10.967490065842867\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.43869960263371466\n",
            "The running loss is:\n",
            "7.611721908673644\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.30446887634694575\n",
            "The running loss is:\n",
            "7.797869371250272\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.31191477485001085\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182258-i4hyn3mi/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182258-i4hyn3mi/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.795289715752006\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.31181158863008024\n",
            "The running loss is:\n",
            "6.245977671816945\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.2498391068726778\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182258-i4hyn3mi/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182258-i4hyn3mi/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.67815606854856\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.2671262427419424\n",
            "Data saved to: \n",
            "28_May_202006_23PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_23PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "INFO:wandb.wandb_agent:Running runs: ['i4hyn3mi']\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  648.033325\n",
            "66  2020-04-22  sub_region  ...             66  696.399780\n",
            "67  2020-04-23  sub_region  ...             67  633.036682\n",
            "68  2020-04-24  sub_region  ...             68  599.434021\n",
            "69  2020-04-25  sub_region  ...             69  679.843323\n",
            "70  2020-04-26  sub_region  ...             70  630.065979\n",
            "71  2020-04-27  sub_region  ...             71  673.871094\n",
            "72  2020-04-28  sub_region  ...             72  681.052856\n",
            "73  2020-04-29  sub_region  ...             73  670.992432\n",
            "74  2020-04-30  sub_region  ...             74  661.747437\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182258-i4hyn3mi/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182258-i4hyn3mi/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182258-i4hyn3mi/wandb-history.jsonl\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: i4hyn3mi \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182258-i4hyn3mi/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182258-i4hyn3mi/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182258-i4hyn3mi/media/plotly/test_plot_20_c2722b2a.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182258-i4hyn3mi/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182258-i4hyn3mi/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182258-i4hyn3mi/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182258-i4hyn3mi/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182258-i4hyn3mi/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: i4hyn3mi\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: lcu7qwcc with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: lcu7qwcc\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/lcu7qwcc\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/lcu7qwcc</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/lcu7qwcc\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmxjdTdxd2NjOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "12.417334482073784\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 0\n",
            "0.5173889367530743\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182310-lcu7qwcc/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182310-lcu7qwcc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182310-lcu7qwcc/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182310-lcu7qwcc/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182310-lcu7qwcc/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182310-lcu7qwcc/media/graph/graph_0_summary_2e285db2.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182310-lcu7qwcc/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182310-lcu7qwcc/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182310-lcu7qwcc/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.187367584556341\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.5911403160231808\n",
            "The running loss is:\n",
            "8.399394869804382\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.34997478624184925\n",
            "1\n",
            "The running loss is:\n",
            "6.990775217302144\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 3\n",
            "0.29128230072092265\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182310-lcu7qwcc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182310-lcu7qwcc/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.946204421576113\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 4\n",
            "0.37275851756567135\n",
            "1\n",
            "The running loss is:\n",
            "15.735674642026424\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n",
            "0.6556531100844344\n",
            "The running loss is:\n",
            "7.7431636694818735\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 6\n",
            "0.32263181956174475\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182310-lcu7qwcc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182310-lcu7qwcc/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "4.328124134801328\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 7\n",
            "0.18033850561672202\n",
            "2\n",
            "The running loss is:\n",
            "3.735455541405827\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 8\n",
            "0.15564398089190945\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182310-lcu7qwcc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182310-lcu7qwcc/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "3.9662495320662856\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 9\n",
            "0.16526039716942856\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202006_23PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_23PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['lcu7qwcc']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  755.517212\n",
            "66  2020-04-22  sub_region  ...             66  771.916016\n",
            "67  2020-04-23  sub_region  ...             67  760.687378\n",
            "68  2020-04-24  sub_region  ...             68  735.305786\n",
            "69  2020-04-25  sub_region  ...             69  728.676880\n",
            "70  2020-04-26  sub_region  ...             70  742.574097\n",
            "71  2020-04-27  sub_region  ...             71  690.534424\n",
            "72  2020-04-28  sub_region  ...             72  759.479492\n",
            "73  2020-04-29  sub_region  ...             73  761.484863\n",
            "74  2020-04-30  sub_region  ...             74  765.487183\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182310-lcu7qwcc/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182310-lcu7qwcc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182310-lcu7qwcc/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182310-lcu7qwcc/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182310-lcu7qwcc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182310-lcu7qwcc/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182310-lcu7qwcc/media/plotly/test_plot_20_1d2f2943.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182310-lcu7qwcc/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182310-lcu7qwcc/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: lcu7qwcc \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182310-lcu7qwcc/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182310-lcu7qwcc/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182310-lcu7qwcc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182310-lcu7qwcc/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182310-lcu7qwcc/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182310-lcu7qwcc/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: lcu7qwcc\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: cra6n8ca with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: cra6n8ca\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/cra6n8ca\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/cra6n8ca</a><br/>\n",
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          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "12.737247206270695\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182325-cra6n8ca/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 0\n",
            "0.5307186335946122\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182325-cra6n8ca/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182325-cra6n8ca/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182325-cra6n8ca/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182325-cra6n8ca/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182325-cra6n8ca/media/graph/graph_0_summary_1b024392.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182325-cra6n8ca/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182325-cra6n8ca/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182325-cra6n8ca/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.846482746303082\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.5352701144292951\n",
            "The running loss is:\n",
            "8.131057146936655\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.3387940477890273\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182325-cra6n8ca/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182325-cra6n8ca/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.895240487996489\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 3\n",
            "0.24563502033318704\n",
            "2\n",
            "The running loss is:\n",
            "8.398755688220263\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 4\n",
            "0.34994815367584425\n",
            "The running loss is:\n",
            "16.694155018776655\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n",
            "0.6955897924490273\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182325-cra6n8ca/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182325-cra6n8ca/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.139933694154024\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 6\n",
            "0.380830570589751\n",
            "1\n",
            "The running loss is:\n",
            "5.126268687658012\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 7\n",
            "0.21359452865241715\n",
            "2\n",
            "The running loss is:\n",
            "4.945361414458603\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 8\n",
            "0.2060567256024418\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_23PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182325-cra6n8ca/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182325-cra6n8ca/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_23PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['cra6n8ca']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  596.721497\n",
            "66  2020-04-22  sub_region  ...             66  611.140503\n",
            "67  2020-04-23  sub_region  ...             67  602.298889\n",
            "68  2020-04-24  sub_region  ...             68  592.272522\n",
            "69  2020-04-25  sub_region  ...             69  590.910889\n",
            "70  2020-04-26  sub_region  ...             70  592.348145\n",
            "71  2020-04-27  sub_region  ...             71  565.893555\n",
            "72  2020-04-28  sub_region  ...             72  597.307617\n",
            "73  2020-04-29  sub_region  ...             73  598.141846\n",
            "74  2020-04-30  sub_region  ...             74  604.241028\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182325-cra6n8ca/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182325-cra6n8ca/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: cra6n8ca \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182325-cra6n8ca/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182325-cra6n8ca/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182325-cra6n8ca/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182325-cra6n8ca/media/plotly/test_plot_all_19_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182325-cra6n8ca/media/plotly/test_plot_18_b0d51432.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182325-cra6n8ca/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182325-cra6n8ca/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182325-cra6n8ca/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: cra6n8ca\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: kbi41ndm with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: kbi41ndm\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/kbi41ndm\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/kbi41ndm</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/kbi41ndm\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmtiaTQxbmRtOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "15.76520562171936\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 0\n",
            "0.65688356757164\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182343-kbi41ndm/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182343-kbi41ndm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182343-kbi41ndm/media/graph/graph_0_summary_e02759de.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182343-kbi41ndm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182343-kbi41ndm/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182343-kbi41ndm/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182343-kbi41ndm/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182343-kbi41ndm/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182343-kbi41ndm/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.477864772081375\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.6449110321700573\n",
            "1\n",
            "The running loss is:\n",
            "12.414291091263294\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.5172621288026372\n",
            "2\n",
            "The running loss is:\n",
            "7.5504014934413135\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 3\n",
            "0.3146000622267214\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182343-kbi41ndm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182343-kbi41ndm/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.823520310223103\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 4\n",
            "0.5343133462592959\n",
            "1\n",
            "The running loss is:\n",
            "6.759511549025774\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n",
            "0.2816463145427406\n",
            "The running loss is:\n",
            "6.30719225294888\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 6\n",
            "0.26279967720620334\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182343-kbi41ndm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182343-kbi41ndm/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.85306416079402\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 7\n",
            "0.28554434003308415\n",
            "2\n",
            "The running loss is:\n",
            "6.9310750886797905\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 8\n",
            "0.2887947953616579\n",
            "The running loss is:\n",
            "8.455319091677666\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 9\n",
            "0.3523049621532361\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202006_23PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182343-kbi41ndm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182343-kbi41ndm/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_23PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['kbi41ndm']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  779.131897\n",
            "66  2020-04-22  sub_region  ...             66  792.555298\n",
            "67  2020-04-23  sub_region  ...             67  780.039795\n",
            "68  2020-04-24  sub_region  ...             68  769.559204\n",
            "69  2020-04-25  sub_region  ...             69  768.763855\n",
            "70  2020-04-26  sub_region  ...             70  775.261108\n",
            "71  2020-04-27  sub_region  ...             71  753.575806\n",
            "72  2020-04-28  sub_region  ...             72  784.202087\n",
            "73  2020-04-29  sub_region  ...             73  783.728699\n",
            "74  2020-04-30  sub_region  ...             74  785.233765\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182343-kbi41ndm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182343-kbi41ndm/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182343-kbi41ndm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182343-kbi41ndm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182343-kbi41ndm/media/plotly/test_plot_20_f7c7c22f.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182343-kbi41ndm/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182343-kbi41ndm/media/plotly\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: kbi41ndm \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182343-kbi41ndm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182343-kbi41ndm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182343-kbi41ndm/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182343-kbi41ndm/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182343-kbi41ndm/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182343-kbi41ndm/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: kbi41ndm\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: tcdi2b90 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: tcdi2b90\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/tcdi2b90\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/tcdi2b90</a><br/>\n",
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          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "15.728306412696838\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 0\n",
            "0.655346100529035\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182355-tcdi2b90/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182355-tcdi2b90/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182355-tcdi2b90/requirements.txt\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.534287229180336\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.5639286345491806\n",
            "1\n",
            "The running loss is:\n",
            "10.27218734100461\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.4280078058751921\n",
            "2\n",
            "The running loss is:\n",
            "5.5147014474496245\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 3\n",
            "0.22977922697706768\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182355-tcdi2b90/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182355-tcdi2b90/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.376355066895485\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 4\n",
            "0.3906814611206452\n",
            "1\n",
            "The running loss is:\n",
            "6.485733915120363\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n",
            "0.27023891313001513\n",
            "2\n",
            "The running loss is:\n",
            "7.979923363775015\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 6\n",
            "0.33249680682395893\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182355-tcdi2b90/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182355-tcdi2b90/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.143040359020233\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 7\n",
            "0.33929334829250973\n",
            "1\n",
            "The running loss is:\n",
            "5.536451553925872\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 8\n",
            "0.230685481413578\n",
            "2\n",
            "The running loss is:\n",
            "4.550636069383472\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 9\n",
            "0.18960983622431135\n",
            "Data saved to: \n",
            "28_May_202006_23PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182355-tcdi2b90/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182355-tcdi2b90/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_23PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['tcdi2b90']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  756.924194\n",
            "66  2020-04-22  sub_region  ...             66  788.874878\n",
            "67  2020-04-23  sub_region  ...             67  767.624146\n",
            "68  2020-04-24  sub_region  ...             68  745.193481\n",
            "69  2020-04-25  sub_region  ...             69  751.710815\n",
            "70  2020-04-26  sub_region  ...             70  755.871094\n",
            "71  2020-04-27  sub_region  ...             71  710.098877\n",
            "72  2020-04-28  sub_region  ...             72  772.029785\n",
            "73  2020-04-29  sub_region  ...             73  776.352173\n",
            "74  2020-04-30  sub_region  ...             74  783.972778\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182355-tcdi2b90/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182355-tcdi2b90/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182355-tcdi2b90/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182355-tcdi2b90/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182355-tcdi2b90/media/plotly/test_plot_20_75431aa1.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182355-tcdi2b90/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182355-tcdi2b90/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: tcdi2b90 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182355-tcdi2b90/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182355-tcdi2b90/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182355-tcdi2b90/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182355-tcdi2b90/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182355-tcdi2b90/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182355-tcdi2b90/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: tcdi2b90\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 9p7a2kaq with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 9p7a2kaq\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/9p7a2kaq\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/9p7a2kaq</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/9p7a2kaq\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjlwN2Eya2FxOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "12.287862300872803\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 0\n",
            "0.5342548826466436\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182412-9p7a2kaq/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182412-9p7a2kaq/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182412-9p7a2kaq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182412-9p7a2kaq/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182412-9p7a2kaq/media/graph/graph_0_summary_220843b4.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182412-9p7a2kaq/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182412-9p7a2kaq/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182412-9p7a2kaq/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182412-9p7a2kaq/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.926748000085354\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 1\n",
            "0.6055107826124067\n",
            "1\n",
            "The running loss is:\n",
            "11.354672580957413\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 2\n",
            "0.4936814165633658\n",
            "2\n",
            "The running loss is:\n",
            "9.702557804062963\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 3\n",
            "0.4218503393070853\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182412-9p7a2kaq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182412-9p7a2kaq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.627340912818909\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 4\n",
            "0.5055365614269091\n",
            "1\n",
            "The running loss is:\n",
            "6.340536650270224\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 5\n",
            "0.275675506533488\n",
            "2\n",
            "The running loss is:\n",
            "10.078678345307708\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 6\n",
            "0.4382034063177264\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182412-9p7a2kaq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182412-9p7a2kaq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.461791813373566\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 7\n",
            "0.3679039918858072\n",
            "1\n",
            "The running loss is:\n",
            "4.9823848977684975\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 8\n",
            "0.21662543033776077\n",
            "2\n",
            "The running loss is:\n",
            "3.317102179862559\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 9\n",
            "0.14422183390706778\n",
            "Data saved to: \n",
            "28_May_202006_24PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182412-9p7a2kaq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182412-9p7a2kaq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_24PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  835.124390\n",
            "66  2020-04-22  sub_region  ...             66  840.772278\n",
            "67  2020-04-23  sub_region  ...             67  832.618164\n",
            "68  2020-04-24  sub_region  ...             68  835.653015\n",
            "69  2020-04-25  sub_region  ...             69  821.924072\n",
            "70  2020-04-26  sub_region  ...             70  833.929749\n",
            "71  2020-04-27  sub_region  ...             71  823.226929\n",
            "72  2020-04-28  sub_region  ...             72  831.859680\n",
            "73  2020-04-29  sub_region  ...             73  828.141541\n",
            "74  2020-04-30  sub_region  ...             74  831.716797\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['9p7a2kaq']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182412-9p7a2kaq/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182412-9p7a2kaq/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 9p7a2kaq \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182412-9p7a2kaq/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182412-9p7a2kaq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182412-9p7a2kaq/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182412-9p7a2kaq/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182412-9p7a2kaq/media/plotly/test_plot_20_7a7a1d5c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182412-9p7a2kaq/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182412-9p7a2kaq/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182412-9p7a2kaq/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 9p7a2kaq\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: a7xv42ra with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: a7xv42ra\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/a7xv42ra\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/a7xv42ra</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/a7xv42ra\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmE3eHY0MnJhOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "12.292471200227737\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 0\n",
            "0.534455269575119\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182423-a7xv42ra/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182423-a7xv42ra/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182423-a7xv42ra/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182423-a7xv42ra/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182423-a7xv42ra/media/graph/graph_0_summary_adccc66b.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182423-a7xv42ra/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182423-a7xv42ra/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182423-a7xv42ra/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182423-a7xv42ra/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.315681867301464\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 1\n",
            "0.6224209507522376\n",
            "1\n",
            "The running loss is:\n",
            "10.289977289736271\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 2\n",
            "0.44739031694505527\n",
            "2\n",
            "The running loss is:\n",
            "7.302962388843298\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 3\n",
            "0.3175201038627521\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182423-a7xv42ra/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182423-a7xv42ra/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.639127224683762\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 4\n",
            "0.462570748899294\n",
            "The running loss is:\n",
            "5.083698073402047\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 5\n",
            "0.2210303510174803\n",
            "1\n",
            "The running loss is:\n",
            "4.266114007681608\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 6\n",
            "0.1854832177252873\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182423-a7xv42ra/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182423-a7xv42ra/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "3.362240646034479\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 7\n",
            "0.14618437591454256\n",
            "The running loss is:\n",
            "5.346718622371554\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 8\n",
            "0.2324660270596328\n",
            "1\n",
            "The running loss is:\n",
            "3.976900838315487\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 9\n",
            "0.17290873210067334\n",
            "Data saved to: \n",
            "28_May_202006_24PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182423-a7xv42ra/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182423-a7xv42ra/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_24PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "INFO:wandb.wandb_agent:Running runs: ['a7xv42ra']\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  773.226318\n",
            "66  2020-04-22  sub_region  ...             66  785.933838\n",
            "67  2020-04-23  sub_region  ...             67  776.867065\n",
            "68  2020-04-24  sub_region  ...             68  779.012268\n",
            "69  2020-04-25  sub_region  ...             69  770.271240\n",
            "70  2020-04-26  sub_region  ...             70  779.572510\n",
            "71  2020-04-27  sub_region  ...             71  765.994873\n",
            "72  2020-04-28  sub_region  ...             72  776.860962\n",
            "73  2020-04-29  sub_region  ...             73  774.669739\n",
            "74  2020-04-30  sub_region  ...             74  777.345093\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182423-a7xv42ra/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182423-a7xv42ra/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: a7xv42ra \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182423-a7xv42ra/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182423-a7xv42ra/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182423-a7xv42ra/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182423-a7xv42ra/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182423-a7xv42ra/media/plotly/test_plot_20_b982c2b4.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182423-a7xv42ra/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182423-a7xv42ra/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182423-a7xv42ra/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: a7xv42ra\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 0c0zfqxx with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 0c0zfqxx\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/0c0zfqxx\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/0c0zfqxx</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/0c0zfqxx\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjBjMHpmcXh4OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182436-0c0zfqxx/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182436-0c0zfqxx/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182436-0c0zfqxx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182436-0c0zfqxx/media/graph/graph_0_summary_4b5baab2.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182436-0c0zfqxx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182436-0c0zfqxx/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182436-0c0zfqxx/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182436-0c0zfqxx/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182436-0c0zfqxx/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.145409000921063\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.4058163600368425\n",
            "The running loss is:\n",
            "25.701497849076986\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "1.0280599139630795\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182436-0c0zfqxx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182436-0c0zfqxx/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.272816001903266\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.8109126400761306\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182436-0c0zfqxx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182436-0c0zfqxx/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.751347083598375\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.790053883343935\n",
            "The running loss is:\n",
            "18.793059289455414\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.7517223715782165\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182436-0c0zfqxx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182436-0c0zfqxx/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.612464647740126\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.664498585909605\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['0c0zfqxx']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.692850414896384\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.38771401659585536\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182436-0c0zfqxx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182436-0c0zfqxx/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.73813858255744\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.7895255433022976\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182436-0c0zfqxx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182436-0c0zfqxx/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.20803095260635\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.488321238104254\n",
            "1\n",
            "The running loss is:\n",
            "11.476102620712481\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.4590441048284993\n",
            "2\n",
            "Data saved to: \n",
            "28_May_202006_24PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182436-0c0zfqxx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182436-0c0zfqxx/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_24PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  801.515869\n",
            "66  2020-04-22  sub_region  ...             66  801.592773\n",
            "67  2020-04-23  sub_region  ...             67  801.521545\n",
            "68  2020-04-24  sub_region  ...             68  801.597168\n",
            "69  2020-04-25  sub_region  ...             69  801.738037\n",
            "70  2020-04-26  sub_region  ...             70  801.605835\n",
            "71  2020-04-27  sub_region  ...             71  801.837097\n",
            "72  2020-04-28  sub_region  ...             72  801.528931\n",
            "73  2020-04-29  sub_region  ...             73  801.504639\n",
            "74  2020-04-30  sub_region  ...             74  801.555054\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182436-0c0zfqxx/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182436-0c0zfqxx/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 0c0zfqxx \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182436-0c0zfqxx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182436-0c0zfqxx/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182436-0c0zfqxx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182436-0c0zfqxx/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182436-0c0zfqxx/media/plotly/test_plot_20_fcc89d57.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182436-0c0zfqxx/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182436-0c0zfqxx/media/plotly\n",
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            "INFO:wandb.wandb_agent:Cleaning up finished run: 0c0zfqxx\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
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            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
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            "wandb: Agent Starting Run: ozprnvr9 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: ozprnvr9\n"
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          "name": "stdout"
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          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/ozprnvr9\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/ozprnvr9</a><br/>\n",
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          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
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        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182453-ozprnvr9/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182453-ozprnvr9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182453-ozprnvr9/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182453-ozprnvr9/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182453-ozprnvr9/media/graph/graph_0_summary_9dca8cf5.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182453-ozprnvr9/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182453-ozprnvr9/wandb-history.jsonl\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182453-ozprnvr9/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.096708084311103\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.40386832337244416\n",
            "The running loss is:\n",
            "25.173049704171717\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "1.0069219881668687\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182453-ozprnvr9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182453-ozprnvr9/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.567884587217122\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.8227153834886849\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182453-ozprnvr9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182453-ozprnvr9/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.932578061707318\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.6773031224682927\n",
            "1\n",
            "The running loss is:\n",
            "11.519542964175344\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.4607817185670137\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182453-ozprnvr9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182453-ozprnvr9/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.808077305089682\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.3523230922035873\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['ozprnvr9']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.38830050965771\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.4555320203863084\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182453-ozprnvr9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182453-ozprnvr9/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.139500390738249\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.28558001562952995\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182453-ozprnvr9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182453-ozprnvr9/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.381748400162905\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.3752699360065162\n",
            "The running loss is:\n",
            "10.521757567301393\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.4208703026920557\n",
            "Data saved to: \n",
            "28_May_202006_25PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182453-ozprnvr9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182453-ozprnvr9/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_25PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
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            "65  2020-04-21  sub_region  ...             65  761.296265\n",
            "66  2020-04-22  sub_region  ...             66  761.453735\n",
            "67  2020-04-23  sub_region  ...             67  761.306885\n",
            "68  2020-04-24  sub_region  ...             68  761.469482\n",
            "69  2020-04-25  sub_region  ...             69  761.735107\n",
            "70  2020-04-26  sub_region  ...             70  761.236694\n",
            "71  2020-04-27  sub_region  ...             71  761.615662\n",
            "72  2020-04-28  sub_region  ...             72  760.781616\n",
            "73  2020-04-29  sub_region  ...             73  760.612366\n",
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            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182453-ozprnvr9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182453-ozprnvr9/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182453-ozprnvr9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182453-ozprnvr9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182453-ozprnvr9/media/plotly/test_plot_20_b3c2d6a3.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182453-ozprnvr9/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182453-ozprnvr9/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: ozprnvr9 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182453-ozprnvr9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182453-ozprnvr9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182453-ozprnvr9/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182453-ozprnvr9/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182453-ozprnvr9/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182453-ozprnvr9/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: ozprnvr9\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: wgc5r97k with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: wgc5r97k\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/wgc5r97k\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/wgc5r97k</a><br/>\n",
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        {
          "output_type": "stream",
          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/wgc5r97k\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOndnYzVyOTdrOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182514-wgc5r97k/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182514-wgc5r97k/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182514-wgc5r97k/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182514-wgc5r97k/media/graph/graph_0_summary_f21185d8.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182514-wgc5r97k/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182514-wgc5r97k/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182514-wgc5r97k/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182514-wgc5r97k/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182514-wgc5r97k/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.584014743566513\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.8233605897426606\n",
            "The running loss is:\n",
            "17.932957649230957\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.7173183059692383\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182514-wgc5r97k/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182514-wgc5r97k/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.548911690013483\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.6219564676005394\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182514-wgc5r97k/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182514-wgc5r97k/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.730191620066762\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.5892076648026705\n",
            "The running loss is:\n",
            "15.559959415346384\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.6223983766138553\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182514-wgc5r97k/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182514-wgc5r97k/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.396017402410507\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.4558406960964203\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['wgc5r97k']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.876246387138963\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.3950498554855585\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_25PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182514-wgc5r97k/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182514-wgc5r97k/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_25PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  505.433014\n",
            "66  2020-04-22  sub_region  ...             66  504.062500\n",
            "67  2020-04-23  sub_region  ...             67  504.517761\n",
            "68  2020-04-24  sub_region  ...             68  503.535156\n",
            "69  2020-04-25  sub_region  ...             69  506.940369\n",
            "70  2020-04-26  sub_region  ...             70  505.692200\n",
            "71  2020-04-27  sub_region  ...             71  506.551117\n",
            "72  2020-04-28  sub_region  ...             72  507.229309\n",
            "73  2020-04-29  sub_region  ...             73  507.427246\n",
            "74  2020-04-30  sub_region  ...             74  507.579987\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182514-wgc5r97k/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182514-wgc5r97k/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182514-wgc5r97k/wandb-summary.json\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182514-wgc5r97k/media\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182514-wgc5r97k/media/plotly\n"
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182514-wgc5r97k/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182514-wgc5r97k/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182514-wgc5r97k/media/plotly/test_plot_all_15_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182514-wgc5r97k/media/plotly\n",
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            "INFO:wandb.wandb_agent:Cleaning up finished run: wgc5r97k\n",
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            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
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            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
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            "wandb: Agent Started Run: 5c9wn3n7\n"
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              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/5c9wn3n7\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/5c9wn3n7</a><br/>\n",
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            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
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            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
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        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182533-5c9wn3n7/config.yaml\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182533-5c9wn3n7/wandb-history.jsonl\n",
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          ],
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          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.51875665783882\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.8207502663135529\n",
            "The running loss is:\n",
            "18.745911203324795\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.7498364481329918\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182533-5c9wn3n7/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182533-5c9wn3n7/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.171599298715591\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.5668639719486237\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182533-5c9wn3n7/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182533-5c9wn3n7/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.58555408520624\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.4234221634082496\n",
            "The running loss is:\n",
            "17.37247646227479\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.6948990584909915\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182533-5c9wn3n7/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182533-5c9wn3n7/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.633864669129252\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.4253545867651701\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['5c9wn3n7']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.219570185989141\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.36878280743956565\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182533-5c9wn3n7/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182533-5c9wn3n7/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.460247997194529\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.33840991988778113\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_25PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_25PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182533-5c9wn3n7/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182533-5c9wn3n7/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182533-5c9wn3n7/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  684.219727\n",
            "66  2020-04-22  sub_region  ...             66  684.323364\n",
            "67  2020-04-23  sub_region  ...             67  684.080750\n",
            "68  2020-04-24  sub_region  ...             68  683.885254\n",
            "69  2020-04-25  sub_region  ...             69  685.422363\n",
            "70  2020-04-26  sub_region  ...             70  684.204346\n",
            "71  2020-04-27  sub_region  ...             71  685.634094\n",
            "72  2020-04-28  sub_region  ...             72  684.494141\n",
            "73  2020-04-29  sub_region  ...             73  684.385315\n",
            "74  2020-04-30  sub_region  ...             74  684.536133\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182533-5c9wn3n7/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182533-5c9wn3n7/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182533-5c9wn3n7/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182533-5c9wn3n7/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182533-5c9wn3n7/media/plotly/test_plot_16_e1926f30.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182533-5c9wn3n7/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182533-5c9wn3n7/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 5c9wn3n7 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182533-5c9wn3n7/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182533-5c9wn3n7/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182533-5c9wn3n7/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182533-5c9wn3n7/media/plotly/test_plot_all_17_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182533-5c9wn3n7/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182533-5c9wn3n7/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 5c9wn3n7\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 63viw4xu with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 63viw4xu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/63viw4xu\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/63viw4xu</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/63viw4xu\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjYzdml3NHh1OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/media/graph/graph_0_summary_3f6952c2.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.772414907813072\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 0\n",
            "0.7821839544922113\n",
            "The running loss is:\n",
            "19.575059436261654\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.8156274765109023\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.316046819090843\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.6381686174621185\n",
            "The running loss is:\n",
            "11.947806634008884\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 3\n",
            "0.49782527641703683\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.35117731243372\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 4\n",
            "0.43129905468473834\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.100783228874207\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n",
            "0.4625326345364253\n",
            "2\n",
            "The running loss is:\n",
            "11.392054236494005\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 6\n",
            "0.4746689265205835\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['63viw4xu']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.173449165187776\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 7\n",
            "0.215560381882824\n",
            "1\n",
            "The running loss is:\n",
            "9.032942300196737\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 8\n",
            "0.3763725958415307\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.382783502340317\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 9\n",
            "0.8076159792641798\n",
            "Data saved to: \n",
            "28_May_202006_25PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_25PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  515.919434\n",
            "66  2020-04-22  sub_region  ...             66  516.543030\n",
            "67  2020-04-23  sub_region  ...             67  515.078186\n",
            "68  2020-04-24  sub_region  ...             68  516.592896\n",
            "69  2020-04-25  sub_region  ...             69  515.011414\n",
            "70  2020-04-26  sub_region  ...             70  514.803894\n",
            "71  2020-04-27  sub_region  ...             71  516.728943\n",
            "72  2020-04-28  sub_region  ...             72  515.620483\n",
            "73  2020-04-29  sub_region  ...             73  515.851013\n",
            "74  2020-04-30  sub_region  ...             74  514.158508\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/media/plotly/test_plot_20_14ad17d9.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 63viw4xu \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182550-63viw4xu/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 63viw4xu\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: qhdtbeld with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: qhdtbeld\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/qhdtbeld\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/qhdtbeld</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/qhdtbeld\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnFoZHRiZWxkOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182608-qhdtbeld/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182608-qhdtbeld/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182608-qhdtbeld/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182608-qhdtbeld/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182608-qhdtbeld/media/graph/graph_0_summary_14707cc8.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182608-qhdtbeld/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182608-qhdtbeld/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182608-qhdtbeld/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182608-qhdtbeld/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.797405660152435\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 0\n",
            "0.7832252358396848\n",
            "The running loss is:\n",
            "19.402401946485043\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.8084334144368768\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182608-qhdtbeld/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182608-qhdtbeld/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.805835217237473\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.616909800718228\n",
            "The running loss is:\n",
            "9.891540326178074\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 3\n",
            "0.4121475135907531\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182608-qhdtbeld/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182608-qhdtbeld/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.6078918017446995\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 4\n",
            "0.23366215840602914\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182608-qhdtbeld/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182608-qhdtbeld/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.041457449435256\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n",
            "0.16839406039313567\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_26PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_26PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['qhdtbeld']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  378.001099\n",
            "66  2020-04-22  sub_region  ...             66  379.296204\n",
            "67  2020-04-23  sub_region  ...             67  378.866272\n",
            "68  2020-04-24  sub_region  ...             68  383.898071\n",
            "69  2020-04-25  sub_region  ...             69  369.884155\n",
            "70  2020-04-26  sub_region  ...             70  381.321381\n",
            "71  2020-04-27  sub_region  ...             71  371.926025\n",
            "72  2020-04-28  sub_region  ...             72  378.183258\n",
            "73  2020-04-29  sub_region  ...             73  379.500549\n",
            "74  2020-04-30  sub_region  ...             74  378.237946\n",
            "\n",
            "[25 rows x 28 columns]"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182608-qhdtbeld/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182608-qhdtbeld/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182608-qhdtbeld/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182608-qhdtbeld/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182608-qhdtbeld/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182608-qhdtbeld/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182608-qhdtbeld/media/plotly/test_plot_12_9498e4f6.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182608-qhdtbeld/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182608-qhdtbeld/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: qhdtbeld \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182608-qhdtbeld/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182608-qhdtbeld/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182608-qhdtbeld/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182608-qhdtbeld/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182608-qhdtbeld/media/plotly/test_plot_all_13_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182608-qhdtbeld/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: qhdtbeld\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: mpoj2ncv with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: mpoj2ncv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/mpoj2ncv\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/mpoj2ncv</a><br/>\n",
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/mpoj2ncv\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOm1wb2oybmN2OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182620-mpoj2ncv/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182620-mpoj2ncv/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182620-mpoj2ncv/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182620-mpoj2ncv/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182620-mpoj2ncv/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182620-mpoj2ncv/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182620-mpoj2ncv/media/graph/graph_0_summary_3a375e0b.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182620-mpoj2ncv/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182620-mpoj2ncv/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.451373293995857\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 0\n",
            "0.8938072205831608\n",
            "The running loss is:\n",
            "18.959119454026222\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.7899633105844259\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182620-mpoj2ncv/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182620-mpoj2ncv/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.811535373330116\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.7004806405554215\n",
            "The running loss is:\n",
            "15.114799819886684\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 3\n",
            "0.6297833258286119\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182620-mpoj2ncv/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182620-mpoj2ncv/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.837378360331059\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 4\n",
            "0.5765574316804608\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182620-mpoj2ncv/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182620-mpoj2ncv/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.938715167343616\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n",
            "0.45577979863931734\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_26PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_26PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['mpoj2ncv']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182620-mpoj2ncv/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182620-mpoj2ncv/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182620-mpoj2ncv/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  379.669922\n",
            "66  2020-04-22  sub_region  ...             66  381.567261\n",
            "67  2020-04-23  sub_region  ...             67  381.375244\n",
            "68  2020-04-24  sub_region  ...             68  386.184875\n",
            "69  2020-04-25  sub_region  ...             69  368.961060\n",
            "70  2020-04-26  sub_region  ...             70  383.574829\n",
            "71  2020-04-27  sub_region  ...             71  370.266907\n",
            "72  2020-04-28  sub_region  ...             72  379.182159\n",
            "73  2020-04-29  sub_region  ...             73  380.702667\n",
            "74  2020-04-30  sub_region  ...             74  380.433716\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182620-mpoj2ncv/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182620-mpoj2ncv/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182620-mpoj2ncv/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182620-mpoj2ncv/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182620-mpoj2ncv/media/plotly/test_plot_12_ab9062b5.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182620-mpoj2ncv/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182620-mpoj2ncv/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: mpoj2ncv \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182620-mpoj2ncv/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182620-mpoj2ncv/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182620-mpoj2ncv/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182620-mpoj2ncv/media/plotly/test_plot_all_13_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182620-mpoj2ncv/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182620-mpoj2ncv/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: mpoj2ncv\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: em108twu with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: em108twu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/em108twu\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/em108twu</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/em108twu\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmVtMTA4dHd1OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182632-em108twu/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182632-em108twu/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182632-em108twu/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182632-em108twu/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182632-em108twu/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182632-em108twu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182632-em108twu/media/graph/graph_0_summary_46e50bbe.graph.json\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182632-em108twu/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.45643301308155\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 0\n",
            "0.8940180422117313\n",
            "The running loss is:\n",
            "18.878746390342712\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.7866144329309464\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182632-em108twu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182632-em108twu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.909177042543888\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.7045490434393287\n",
            "The running loss is:\n",
            "14.992017075419426\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 3\n",
            "0.6246673781424761\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182632-em108twu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182632-em108twu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.057133480906487\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 4\n",
            "0.5440472283711036\n",
            "2\n",
            "The running loss is:\n",
            "7.168162252753973\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182632-em108twu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182632-em108twu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0.2986734271980822\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_26PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_26PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['em108twu']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182632-em108twu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182632-em108twu/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182632-em108twu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  381.720642\n",
            "66  2020-04-22  sub_region  ...             66  383.487915\n",
            "67  2020-04-23  sub_region  ...             67  382.618774\n",
            "68  2020-04-24  sub_region  ...             68  386.354279\n",
            "69  2020-04-25  sub_region  ...             69  371.824524\n",
            "70  2020-04-26  sub_region  ...             70  384.303436\n",
            "71  2020-04-27  sub_region  ...             71  373.255554\n",
            "72  2020-04-28  sub_region  ...             72  381.710022\n",
            "73  2020-04-29  sub_region  ...             73  383.322937\n",
            "74  2020-04-30  sub_region  ...             74  382.421906\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182632-em108twu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182632-em108twu/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182632-em108twu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182632-em108twu/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182632-em108twu/media/plotly/test_plot_12_d4e101e3.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182632-em108twu/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182632-em108twu/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: em108twu \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182632-em108twu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182632-em108twu/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182632-em108twu/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182632-em108twu/media/plotly/test_plot_all_13_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182632-em108twu/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182632-em108twu/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: em108twu\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: ecutz549 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: ecutz549\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/ecutz549\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/ecutz549</a><br/>\n",
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          "output_type": "stream",
          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/ecutz549\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmVjdXR6NTQ5OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/media/graph/graph_0_summary_a3da4031.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.729351297020912\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 0\n",
            "0.7708413607400396\n",
            "The running loss is:\n",
            "17.55451713502407\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 1\n",
            "0.7632398754358292\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.769226357340813\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 2\n",
            "0.5986620155365571\n",
            "1\n",
            "The running loss is:\n",
            "10.689910009503365\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 3\n",
            "0.46477869606536365\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.719441205263138\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 4\n",
            "0.4660626610983973\n",
            "The running loss is:\n",
            "11.102143481373787\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 5\n",
            "0.48270189049451245\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.552160829305649\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 6\n",
            "0.3718330795350282\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['ecutz549']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.911982571706176\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 7\n",
            "0.3874775031176598\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "10.839222326874733\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 8\n",
            "0.4712705359510753\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.471042025834322\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 9\n",
            "0.28134965329714445\n",
            "Data saved to: \n",
            "28_May_202006_26PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_26PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  682.042175\n",
            "66  2020-04-22  sub_region  ...             66  684.554626\n",
            "67  2020-04-23  sub_region  ...             67  676.001160\n",
            "68  2020-04-24  sub_region  ...             68  688.336792\n",
            "69  2020-04-25  sub_region  ...             69  666.439087\n",
            "70  2020-04-26  sub_region  ...             70  671.417664\n",
            "71  2020-04-27  sub_region  ...             71  689.934692\n",
            "72  2020-04-28  sub_region  ...             72  674.941956\n",
            "73  2020-04-29  sub_region  ...             73  665.878967\n",
            "74  2020-04-30  sub_region  ...             74  658.025818\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/wandb-summary.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/media/plotly/test_plot_20_790f76c3.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: ecutz549 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182649-ecutz549/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: ecutz549\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: m7ynw4z3 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: m7ynw4z3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/m7ynw4z3\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/m7ynw4z3</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/m7ynw4z3\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOm03eW53NHozOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182708-m7ynw4z3/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.749317228794098\n",
            "The number of items in train is: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182708-m7ynw4z3/wandb-metadata.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "23\n",
            "The loss for epoch 0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182708-m7ynw4z3/wandb-events.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0.7717094447301782\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182708-m7ynw4z3/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182708-m7ynw4z3/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182708-m7ynw4z3/media/graph/graph_0_summary_654b7d89.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182708-m7ynw4z3/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182708-m7ynw4z3/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182708-m7ynw4z3/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.551174700260162\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 1\n",
            "0.7630945521852245\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182708-m7ynw4z3/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182708-m7ynw4z3/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.54475101083517\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 2\n",
            "0.5889022178623987\n",
            "1\n",
            "The running loss is:\n",
            "10.344177260994911\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 3\n",
            "0.44974683743456134\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182708-m7ynw4z3/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182708-m7ynw4z3/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.028607338666916\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 4\n",
            "0.39254814515943115\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_27PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_27PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182708-m7ynw4z3/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182708-m7ynw4z3/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182708-m7ynw4z3/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  391.728210\n",
            "66  2020-04-22  sub_region  ...             66  392.369507\n",
            "67  2020-04-23  sub_region  ...             67  392.830872\n",
            "68  2020-04-24  sub_region  ...             68  397.064850\n",
            "69  2020-04-25  sub_region  ...             69  385.320221\n",
            "70  2020-04-26  sub_region  ...             70  393.841858\n",
            "71  2020-04-27  sub_region  ...             71  388.418610\n",
            "72  2020-04-28  sub_region  ...             72  390.187714\n",
            "73  2020-04-29  sub_region  ...             73  390.757416\n",
            "74  2020-04-30  sub_region  ...             74  391.191467\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['m7ynw4z3']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182708-m7ynw4z3/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182708-m7ynw4z3/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182708-m7ynw4z3/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182708-m7ynw4z3/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182708-m7ynw4z3/media/plotly/test_plot_10_ca77e0e0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182708-m7ynw4z3/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182708-m7ynw4z3/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: m7ynw4z3 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182708-m7ynw4z3/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182708-m7ynw4z3/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182708-m7ynw4z3/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182708-m7ynw4z3/media/plotly/test_plot_all_11_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182708-m7ynw4z3/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182708-m7ynw4z3/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: m7ynw4z3\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: ixe38oga with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: ixe38oga\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/ixe38oga\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/ixe38oga</a><br/>\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/ixe38oga\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOml4ZTM4b2dhOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
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          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
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        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182722-ixe38oga/config.yaml\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182722-ixe38oga/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182722-ixe38oga/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182722-ixe38oga/requirements.txt\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.765136603731662\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.4306054641492665\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182722-ixe38oga/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182722-ixe38oga/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "26.137935783714056\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "1.0455174313485622\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182722-ixe38oga/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182722-ixe38oga/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.011536345118657\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.8804614538047463\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182722-ixe38oga/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182722-ixe38oga/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.903898189775646\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.7561559275910258\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['ixe38oga']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.865029871463776\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.754601194858551\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182722-ixe38oga/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182722-ixe38oga/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.70023591836798\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.7880094367347192\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182722-ixe38oga/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182722-ixe38oga/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.773742999881506\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.7509497199952603\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182722-ixe38oga/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182722-ixe38oga/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.47792062163353\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.7791168248653412\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182722-ixe38oga/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182722-ixe38oga/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.640535548329353\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.7856214219331741\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182722-ixe38oga/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.046687852591276\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.7618675141036511\n",
            "Data saved to: \n",
            "28_May_202006_27PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_27PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182722-ixe38oga/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182722-ixe38oga/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182722-ixe38oga/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
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          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182722-ixe38oga/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182722-ixe38oga/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: ixe38oga \n",
            "\n"
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          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182722-ixe38oga/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182722-ixe38oga/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182722-ixe38oga/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182722-ixe38oga/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182722-ixe38oga/media/plotly/test_plot_20_59b2352c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182722-ixe38oga/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182722-ixe38oga/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182722-ixe38oga/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: ixe38oga\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 2g463y10 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 2g463y10\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/2g463y10\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/2g463y10</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/2g463y10\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjJnNDYzeTEwOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
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        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/media/graph/graph_0_summary_42cee6f6.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.082796600530855\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.44331186402123424\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "26.767839308828115\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "1.0707135723531247\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.91911711357534\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.7967646845430135\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.481270607560873\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.7392508243024349\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['2g463y10']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.93701122701168\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.7574804490804672\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.590259796008468\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.7836103918403388\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.897615873254836\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.7559046349301934\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.6400615721941\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.785602462887764\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.225644499063492\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.7690257799625396\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.104803455993533\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.7641921382397413\n",
            "2\n",
            "Data saved to: \n",
            "28_May_202006_27PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_27PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  495.774384\n",
            "66  2020-04-22  sub_region  ...             66  495.760284\n",
            "67  2020-04-23  sub_region  ...             67  495.068512\n",
            "68  2020-04-24  sub_region  ...             68  496.070099\n",
            "69  2020-04-25  sub_region  ...             69  497.589020\n",
            "70  2020-04-26  sub_region  ...             70  495.803619\n",
            "71  2020-04-27  sub_region  ...             71  500.093933\n",
            "72  2020-04-28  sub_region  ...             72  495.899109\n",
            "73  2020-04-29  sub_region  ...             73  495.294128\n",
            "74  2020-04-30  sub_region  ...             74  495.290405\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/media/plotly/test_plot_20_b4b150f6.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/media\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 2g463y10 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182744-2g463y10/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 2g463y10\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: jnu41qmm with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: jnu41qmm\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/jnu41qmm\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/jnu41qmm</a><br/>\n",
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          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/jnu41qmm\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmpudTQxcW1tOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182807-jnu41qmm/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182807-jnu41qmm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182807-jnu41qmm/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182807-jnu41qmm/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182807-jnu41qmm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182807-jnu41qmm/requirements.txt\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.70665431022644\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.9082661724090576\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182807-jnu41qmm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182807-jnu41qmm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182807-jnu41qmm/media/graph/graph_0_summary_7d609e50.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182807-jnu41qmm/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182807-jnu41qmm/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.07255493104458\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.8029021972417831\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182807-jnu41qmm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182807-jnu41qmm/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.82785291969776\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.7931141167879104\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182807-jnu41qmm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182807-jnu41qmm/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.11555242538452\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.8446220970153808\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['jnu41qmm']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182807-jnu41qmm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182807-jnu41qmm/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.238652385771275\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.769546095430851\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182807-jnu41qmm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182807-jnu41qmm/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.169510439038277\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.766780417561531\n",
            "2\n",
            "The running loss is:\n",
            "19.454397417604923\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.778175896704197\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182807-jnu41qmm/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "28_May_202006_28PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182807-jnu41qmm/wandb-history.jsonl\n",
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_28PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  386.295502\n",
            "66  2020-04-22  sub_region  ...             66  386.368958\n",
            "67  2020-04-23  sub_region  ...             67  386.170776\n",
            "68  2020-04-24  sub_region  ...             68  386.041290\n",
            "69  2020-04-25  sub_region  ...             69  386.396393\n",
            "70  2020-04-26  sub_region  ...             70  386.061401\n",
            "71  2020-04-27  sub_region  ...             71  386.280853\n",
            "72  2020-04-28  sub_region  ...             72  386.269623\n",
            "73  2020-04-29  sub_region  ...             73  386.305389\n",
            "74  2020-04-30  sub_region  ...             74  386.221985\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182807-jnu41qmm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182807-jnu41qmm/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: jnu41qmm \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182807-jnu41qmm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182807-jnu41qmm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182807-jnu41qmm/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182807-jnu41qmm/media/plotly/test_plot_14_416120dd.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182807-jnu41qmm/media/plotly/test_plot_all_15_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182807-jnu41qmm/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182807-jnu41qmm/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182807-jnu41qmm/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: jnu41qmm\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: le9gifcu with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: le9gifcu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/le9gifcu\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/le9gifcu</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/le9gifcu\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmxlOWdpZmN1OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182824-le9gifcu/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182824-le9gifcu/media/graph/graph_0_summary_953bc13a.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182824-le9gifcu/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182824-le9gifcu/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182824-le9gifcu/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182824-le9gifcu/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182824-le9gifcu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182824-le9gifcu/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182824-le9gifcu/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.725555673241615\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.9090222269296646\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182824-le9gifcu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182824-le9gifcu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.579730972647667\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.8231892389059067\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182824-le9gifcu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182824-le9gifcu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.793140269815922\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.7917256107926369\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182824-le9gifcu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182824-le9gifcu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.604701928794384\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.8241880771517753\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['le9gifcu']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.242981515824795\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.7697192606329918\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182824-le9gifcu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182824-le9gifcu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.15913762152195\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.766365504860878\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182824-le9gifcu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182824-le9gifcu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.610484287142754\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.7844193714857102\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_28PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182824-le9gifcu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182824-le9gifcu/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_28PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  408.607574\n",
            "66  2020-04-22  sub_region  ...             66  408.763275\n",
            "67  2020-04-23  sub_region  ...             67  408.592804\n",
            "68  2020-04-24  sub_region  ...             68  408.707031\n",
            "69  2020-04-25  sub_region  ...             69  408.915588\n",
            "70  2020-04-26  sub_region  ...             70  408.787262\n",
            "71  2020-04-27  sub_region  ...             71  409.198151\n",
            "72  2020-04-28  sub_region  ...             72  408.587250\n",
            "73  2020-04-29  sub_region  ...             73  408.494568\n",
            "74  2020-04-30  sub_region  ...             74  408.603363\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182824-le9gifcu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182824-le9gifcu/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: le9gifcu \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182824-le9gifcu/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182824-le9gifcu/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182824-le9gifcu/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182824-le9gifcu/media/plotly/test_plot_14_755375b7.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182824-le9gifcu/media/plotly/test_plot_all_15_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182824-le9gifcu/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182824-le9gifcu/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182824-le9gifcu/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: le9gifcu\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: p93c9oel with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: p93c9oel\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/p93c9oel\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/p93c9oel</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/p93c9oel\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnA5M2M5b2VsOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/media/graph/graph_0_summary_02eaa5ab.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.80333824455738\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 0\n",
            "0.8251390935232242\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.39603229612112\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.93316801233838\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.272497054189444\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.5946873772578934\n",
            "1\n",
            "The running loss is:\n",
            "14.552330110222101\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 3\n",
            "0.6063470879259208\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.988487225025892\n",
            "The number of items in train is: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['p93c9oel']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "24\n",
            "The loss for epoch 4\n",
            "0.5828536343760788\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.61138915270567\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n",
            "0.6088078813627362\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.06570915877819\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 6\n",
            "0.5444045482824246\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.021730467677116\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 7\n",
            "0.41757210281987983\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.953084118664265\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 8\n",
            "0.49804517161101103\n",
            "1\n",
            "The running loss is:\n",
            "10.495671391487122\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 9\n",
            "0.4373196413119634\n",
            "2\n",
            "Data saved to: \n",
            "28_May_202006_28PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_28PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  705.102661\n",
            "66  2020-04-22  sub_region  ...             66  705.103027\n",
            "67  2020-04-23  sub_region  ...             67  705.096924\n",
            "68  2020-04-24  sub_region  ...             68  705.107422\n",
            "69  2020-04-25  sub_region  ...             69  705.110229\n",
            "70  2020-04-26  sub_region  ...             70  705.101257\n",
            "71  2020-04-27  sub_region  ...             71  705.131958\n",
            "72  2020-04-28  sub_region  ...             72  705.105957\n",
            "73  2020-04-29  sub_region  ...             73  705.100586\n",
            "74  2020-04-30  sub_region  ...             74  705.097656\n",
            "\n",
            "[25 rows x 28 columns]"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/media/plotly/test_plot_20_2b3d8713.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: p93c9oel \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/wandb-events.jsonl\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182844-p93c9oel/media/plotly\n",
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            "INFO:wandb.wandb_agent:Cleaning up finished run: p93c9oel\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
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            "wandb: Agent Starting Run: 6g3htrhm with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 6g3htrhm\n"
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          "name": "stdout"
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          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/6g3htrhm\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/6g3htrhm</a><br/>\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/6g3htrhm\n",
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          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
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          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/media/graph/graph_0_summary_8c905f4c.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/wandb-summary.json\n",
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          ],
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        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.914355665445328\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 0\n",
            "0.8297648193935553\n"
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        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/wandb-history.jsonl\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.440757513046265\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.9350315630435944\n"
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          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.276884399354458\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.5948701833064357\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.543040201067924\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 3\n",
            "0.6059600083778302\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['6g3htrhm']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.132904570549726\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 4\n",
            "0.5888710237729052\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.135652396827936\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n",
            "0.5473188498678306\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.404533512890339\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 6\n",
            "0.5168555630370975\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.101209737360477\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 7\n",
            "0.5042170723900199\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.456808343529701\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 8\n",
            "0.6023670143137375\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.79074577242136\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 9\n",
            "0.36628107385089\n",
            "2\n",
            "Data saved to: \n",
            "28_May_202006_29PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_29PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/wandb-history.jsonl\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/config.yaml\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  748.224915\n",
            "66  2020-04-22  sub_region  ...             66  748.223572\n",
            "67  2020-04-23  sub_region  ...             67  748.228333\n",
            "68  2020-04-24  sub_region  ...             68  748.199829\n",
            "69  2020-04-25  sub_region  ...             69  748.202820\n",
            "70  2020-04-26  sub_region  ...             70  748.207642\n",
            "71  2020-04-27  sub_region  ...             71  748.156372\n",
            "72  2020-04-28  sub_region  ...             72  748.215271\n",
            "73  2020-04-29  sub_region  ...             73  748.225708\n",
            "74  2020-04-30  sub_region  ...             74  748.218994\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/media/plotly/test_plot_20_62c51e74.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/media\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 6g3htrhm \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182906-6g3htrhm/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 6g3htrhm\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: kxdboz50 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: kxdboz50\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/kxdboz50\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/kxdboz50</a><br/>\n",
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          "metadata": {
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/kxdboz50\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmt4ZGJvejUwOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/media/graph/graph_0_summary_507531e2.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.587218046188354\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 0\n",
            "0.9411340852578481\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.35363259911537\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.8064013582964739\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.189658485352993\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.6745691035563747\n",
            "The running loss is:\n",
            "15.560340829193592\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 3\n",
            "0.648347534549733\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.733900420367718\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 4\n",
            "0.6555791841819882\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['kxdboz50']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.708370111882687\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n",
            "0.6545154213284453\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.470614947378635\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 6\n",
            "0.6446089561407765\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.394039362668991\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 7\n",
            "0.6414183067778746\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.352656692266464\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 8\n",
            "0.639694028844436\n",
            "The running loss is:\n",
            "15.739529885351658\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 9\n",
            "0.6558137452229857\n",
            "1\n",
            "Data saved to: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "28_May_202006_29PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/wandb-history.jsonl\n",
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_29PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  404.308929\n",
            "66  2020-04-22  sub_region  ...             66  405.262390\n",
            "67  2020-04-23  sub_region  ...             67  405.003235\n",
            "68  2020-04-24  sub_region  ...             68  406.038116\n",
            "69  2020-04-25  sub_region  ...             69  403.401398\n",
            "70  2020-04-26  sub_region  ...             70  406.035461\n",
            "71  2020-04-27  sub_region  ...             71  402.491180\n",
            "72  2020-04-28  sub_region  ...             72  404.122070\n",
            "73  2020-04-29  sub_region  ...             73  404.753174\n",
            "74  2020-04-30  sub_region  ...             74  405.485504\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/media/plotly/test_plot_20_dcd65458.plotly.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: kxdboz50 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182929-kxdboz50/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: kxdboz50\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: ppzooyba with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: ppzooyba\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/ppzooyba\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/ppzooyba</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/ppzooyba\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnBwem9veWJhOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182951-ppzooyba/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182951-ppzooyba/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182951-ppzooyba/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182951-ppzooyba/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182951-ppzooyba/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182951-ppzooyba/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182951-ppzooyba/media/graph/graph_0_summary_a7d93b40.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182951-ppzooyba/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182951-ppzooyba/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.53992810845375\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 0\n",
            "0.939163671185573\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182951-ppzooyba/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182951-ppzooyba/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.258116833865643\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.8024215347444018\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182951-ppzooyba/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182951-ppzooyba/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.27618619799614\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.6781744249165058\n",
            "The running loss is:\n",
            "15.538168050348759\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 3\n",
            "0.6474236687645316\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182951-ppzooyba/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182951-ppzooyba/wandb-history.jsonl\n",
            "INFO:wandb.wandb_agent:Running runs: ['ppzooyba']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.75120346993208\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 4\n",
            "0.6563001445805033\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182951-ppzooyba/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182951-ppzooyba/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.720529459416866\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n",
            "0.6550220608090361\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182951-ppzooyba/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182951-ppzooyba/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.435486644506454\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 6\n",
            "0.6431452768544356\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182951-ppzooyba/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182951-ppzooyba/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.386884167790413\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 7\n",
            "0.6411201736579338\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182951-ppzooyba/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182951-ppzooyba/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.28485395014286\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 8\n",
            "0.6368689145892859\n",
            "The running loss is:\n",
            "15.743594877421856\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 9\n",
            "0.6559831198925773\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182951-ppzooyba/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182951-ppzooyba/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "Data saved to: \n",
            "28_May_202006_30PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_30PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  400.785828\n",
            "66  2020-04-22  sub_region  ...             66  401.776733\n",
            "67  2020-04-23  sub_region  ...             67  401.619812\n",
            "68  2020-04-24  sub_region  ...             68  403.111847\n",
            "69  2020-04-25  sub_region  ...             69  400.459259\n",
            "70  2020-04-26  sub_region  ...             70  402.953522\n",
            "71  2020-04-27  sub_region  ...             71  400.775208\n",
            "72  2020-04-28  sub_region  ...             72  400.355286\n",
            "73  2020-04-29  sub_region  ...             73  400.579224\n",
            "74  2020-04-30  sub_region  ...             74  401.702911\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182951-ppzooyba/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182951-ppzooyba/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182951-ppzooyba/wandb-history.jsonl\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182951-ppzooyba/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182951-ppzooyba/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182951-ppzooyba/media/plotly/test_plot_20_42d1f4aa.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182951-ppzooyba/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182951-ppzooyba/media/plotly\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: ppzooyba \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_182951-ppzooyba/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: ppzooyba\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
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            "\tforecast_history: 15\n",
            "\tlr: 0.002\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: tbmo4dqz\n"
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            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/tbmo4dqz\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/tbmo4dqz</a><br/>\n",
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            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
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            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.002\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.002\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
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          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183009-tbmo4dqz/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183009-tbmo4dqz/wandb-summary.json\n",
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        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.067732363939285\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 0\n",
            "0.8290318419104037\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183009-tbmo4dqz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183009-tbmo4dqz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.485696509480476\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 1\n",
            "0.8037259351948033\n",
            "The running loss is:\n",
            "13.346832662820816\n",
            "The number of items in train is: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183009-tbmo4dqz/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "23\n",
            "The loss for epoch 2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183009-tbmo4dqz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0.5802970722965572\n",
            "The running loss is:\n",
            "13.152954459190369\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 3\n",
            "0.5718675851821899\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183009-tbmo4dqz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183009-tbmo4dqz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.9220210313797\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 4\n",
            "0.5183487404947695\n"
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          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['tbmo4dqz']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183009-tbmo4dqz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183009-tbmo4dqz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.841944128274918\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 5\n",
            "0.38443235340325727\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183009-tbmo4dqz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183009-tbmo4dqz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.500840276479721\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 6\n",
            "0.5000365337599879\n",
            "The running loss is:\n",
            "7.89447258785367\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 7\n",
            "0.3432379386023335\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183009-tbmo4dqz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183009-tbmo4dqz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "7.601438160985708\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 8\n",
            "0.3304973113472047\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183009-tbmo4dqz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183009-tbmo4dqz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.348183668218553\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 9\n",
            "0.18905146383558927\n",
            "Data saved to: \n",
            "28_May_202006_30PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183009-tbmo4dqz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183009-tbmo4dqz/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_30PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  752.961365\n",
            "66  2020-04-22  sub_region  ...             66  752.971558\n",
            "67  2020-04-23  sub_region  ...             67  752.971436\n",
            "68  2020-04-24  sub_region  ...             68  752.982910\n",
            "69  2020-04-25  sub_region  ...             69  753.031128\n",
            "70  2020-04-26  sub_region  ...             70  753.011536\n",
            "71  2020-04-27  sub_region  ...             71  753.077209\n",
            "72  2020-04-28  sub_region  ...             72  752.963745\n",
            "73  2020-04-29  sub_region  ...             73  752.952332\n",
            "74  2020-04-30  sub_region  ...             74  752.973022\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183009-tbmo4dqz/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183009-tbmo4dqz/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: tbmo4dqz \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183009-tbmo4dqz/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183009-tbmo4dqz/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183009-tbmo4dqz/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183009-tbmo4dqz/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183009-tbmo4dqz/media/plotly/test_plot_20_b60ea359.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183009-tbmo4dqz/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183009-tbmo4dqz/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183009-tbmo4dqz/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: tbmo4dqz\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Network error resolved after 0:00:13.296214, resuming normal operation.\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 6mksv9sd with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 6mksv9sd\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/6mksv9sd\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/6mksv9sd</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/6mksv9sd\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjZta3N2OXNkOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183037-6mksv9sd/config.yaml\n",
            "INFO:wandb.wandb_agent:Running runs: ['6mksv9sd']\n",
            "ERROR:wandb.run_manager:Failed to connect to W&B servers after 10 seconds.                    Letting user process proceed while attempting to reconnect.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "13.29942782886792\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.5319771131547167\n",
            "The running loss is:\n",
            "15.958505789749324\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.638340231589973\n",
            "The running loss is:\n",
            "9.610189565457404\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.38440758261829616\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Network error resolved after 0:00:11.353098, resuming normal operation.\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183037-6mksv9sd/wandb-history.jsonl\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183037-6mksv9sd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183037-6mksv9sd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183037-6mksv9sd/config.yaml\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183037-6mksv9sd/media/graph\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183037-6mksv9sd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183037-6mksv9sd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183037-6mksv9sd/requirements.txt\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.331762389920186\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.33327049559680744\n",
            "1\n",
            "The running loss is:\n",
            "6.60916328427993\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.2643665313711972\n",
            "2\n",
            "The running loss is:\n",
            "6.482465127395699\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.25929860509582797\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183037-6mksv9sd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183037-6mksv9sd/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.1515498894732445\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.2860619955789298\n",
            "The running loss is:\n",
            "6.881212463777047\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.27524849855108185\n",
            "The running loss is:\n",
            "7.631635777652264\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.30526543110609056\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183037-6mksv9sd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183037-6mksv9sd/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.2557811347651295\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.2502312453906052\n",
            "Data saved to: \n",
            "28_May_202006_30PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_30PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183037-6mksv9sd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183037-6mksv9sd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183037-6mksv9sd/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  801.962341\n",
            "66  2020-04-22  sub_region  ...             66  824.838013\n",
            "67  2020-04-23  sub_region  ...             67  775.098450\n",
            "68  2020-04-24  sub_region  ...             68  820.728394\n",
            "69  2020-04-25  sub_region  ...             69  858.230591\n",
            "70  2020-04-26  sub_region  ...             70  790.655151\n",
            "71  2020-04-27  sub_region  ...             71  957.766296\n",
            "72  2020-04-28  sub_region  ...             72  793.469360\n",
            "73  2020-04-29  sub_region  ...             73  769.994263\n",
            "74  2020-04-30  sub_region  ...             74  771.788574\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183037-6mksv9sd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183037-6mksv9sd/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183037-6mksv9sd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183037-6mksv9sd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183037-6mksv9sd/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183037-6mksv9sd/media/plotly/test_plot_20_302bf663.plotly.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183037-6mksv9sd/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183037-6mksv9sd/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 6mksv9sd \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183037-6mksv9sd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183037-6mksv9sd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183037-6mksv9sd/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183037-6mksv9sd/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183037-6mksv9sd/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183037-6mksv9sd/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 6mksv9sd\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 1na747h3 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 1na747h3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/1na747h3\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/1na747h3</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/1na747h3\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjFuYTc0N2gzOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "13.510150267975405\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.5404060107190162\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183105-1na747h3/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183105-1na747h3/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183105-1na747h3/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183105-1na747h3/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183105-1na747h3/media/graph/graph_0_summary_4bbab05e.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183105-1na747h3/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183105-1na747h3/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183105-1na747h3/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183105-1na747h3/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.415556019172072\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.6566222407668829\n",
            "1\n",
            "The running loss is:\n",
            "8.791841979138553\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.35167367916554215\n",
            "The running loss is:\n",
            "9.764055475010537\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.3905622190004215\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183105-1na747h3/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183105-1na747h3/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.065425713779405\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.2826170285511762\n",
            "1\n",
            "The running loss is:\n",
            "6.248815768616623\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.24995263074466492\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183105-1na747h3/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183105-1na747h3/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.674299073871225\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.266971962954849\n",
            "1\n",
            "The running loss is:\n",
            "7.030096196685918\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.2812038478674367\n",
            "The running loss is:\n",
            "8.11010273359716\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.32440410934388636\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183105-1na747h3/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183105-1na747h3/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.296146783512086\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.33184587134048343\n",
            "Data saved to: \n",
            "28_May_202006_31PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_31PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['1na747h3']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  725.814941\n",
            "66  2020-04-22  sub_region  ...             66  739.527832\n",
            "67  2020-04-23  sub_region  ...             67  716.998169\n",
            "68  2020-04-24  sub_region  ...             68  738.462158\n",
            "69  2020-04-25  sub_region  ...             69  756.830688\n",
            "70  2020-04-26  sub_region  ...             70  728.941650\n",
            "71  2020-04-27  sub_region  ...             71  807.169800\n",
            "72  2020-04-28  sub_region  ...             72  725.725586\n",
            "73  2020-04-29  sub_region  ...             73  714.828308\n",
            "74  2020-04-30  sub_region  ...             74  716.417358\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183105-1na747h3/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183105-1na747h3/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183105-1na747h3/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 1na747h3 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183105-1na747h3/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183105-1na747h3/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183105-1na747h3/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183105-1na747h3/media/plotly/test_plot_20_f6cc8bc8.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183105-1na747h3/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183105-1na747h3/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183105-1na747h3/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183105-1na747h3/wandb-events.jsonl\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 1na747h3\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: t27i38bn with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: t27i38bn\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/t27i38bn\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/t27i38bn</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/t27i38bn\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnQyN2kzOGJuOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "19.00246888399124\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.7600987553596497\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183122-t27i38bn/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183122-t27i38bn/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183122-t27i38bn/media/graph/graph_0_summary_cb13d26f.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183122-t27i38bn/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183122-t27i38bn/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183122-t27i38bn/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183122-t27i38bn/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183122-t27i38bn/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183122-t27i38bn/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.036757367663085\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.6414702947065234\n",
            "1\n",
            "The running loss is:\n",
            "10.140504654031247\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.4056201861612499\n",
            "The running loss is:\n",
            "11.365801957435906\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.45463207829743624\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183122-t27i38bn/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183122-t27i38bn/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "15.48135045915842\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.6192540183663369\n",
            "2\n",
            "The running loss is:\n",
            "8.226837230846286\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.32907348923385144\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183122-t27i38bn/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.584667568095028\n",
            "The number of items in train is: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183122-t27i38bn/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "25\n",
            "The loss for epoch 6\n",
            "0.30338670272380114\n",
            "1\n",
            "The running loss is:\n",
            "8.201896103098989\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.3280758441239595\n",
            "2\n",
            "The running loss is:\n",
            "8.102215321734548\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.3240886128693819\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183122-t27i38bn/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183122-t27i38bn/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.849566355347633\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.35398265421390535\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202006_31PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_31PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['t27i38bn']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183122-t27i38bn/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183122-t27i38bn/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183122-t27i38bn/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  721.418884\n",
            "66  2020-04-22  sub_region  ...             66  729.418823\n",
            "67  2020-04-23  sub_region  ...             67  704.071716\n",
            "68  2020-04-24  sub_region  ...             68  698.017456\n",
            "69  2020-04-25  sub_region  ...             69  721.563843\n",
            "70  2020-04-26  sub_region  ...             70  702.487244\n",
            "71  2020-04-27  sub_region  ...             71  729.263550\n",
            "72  2020-04-28  sub_region  ...             72  726.579834\n",
            "73  2020-04-29  sub_region  ...             73  722.222656\n",
            "74  2020-04-30  sub_region  ...             74  718.942139\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183122-t27i38bn/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183122-t27i38bn/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183122-t27i38bn/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183122-t27i38bn/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183122-t27i38bn/media/plotly/test_plot_20_359c7a7b.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183122-t27i38bn/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183122-t27i38bn/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: t27i38bn \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183122-t27i38bn/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183122-t27i38bn/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183122-t27i38bn/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183122-t27i38bn/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183122-t27i38bn/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183122-t27i38bn/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: t27i38bn\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: r7sxv2qw with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: r7sxv2qw\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/r7sxv2qw\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/r7sxv2qw</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/r7sxv2qw\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnI3c3h2MnF3OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "18.724818095564842\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.7489927238225937\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183135-r7sxv2qw/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183135-r7sxv2qw/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183135-r7sxv2qw/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183135-r7sxv2qw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183135-r7sxv2qw/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183135-r7sxv2qw/media/graph/graph_0_summary_2ebd3a78.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183135-r7sxv2qw/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183135-r7sxv2qw/media/graph\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.727116253226995\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.6290846501290798\n",
            "1\n",
            "The running loss is:\n",
            "8.56138514901977\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.3424554059607908\n",
            "The running loss is:\n",
            "10.345093304291368\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.4138037321716547\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183135-r7sxv2qw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183135-r7sxv2qw/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "10.967490065842867\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.43869960263371466\n",
            "The running loss is:\n",
            "7.611721908673644\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.30446887634694575\n",
            "The running loss is:\n",
            "7.797869371250272\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.31191477485001085\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183135-r7sxv2qw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183135-r7sxv2qw/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.795289715752006\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.31181158863008024\n",
            "The running loss is:\n",
            "6.245977671816945\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.2498391068726778\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183135-r7sxv2qw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183135-r7sxv2qw/wandb-history.jsonl\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.67815606854856\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.2671262427419424\n",
            "Data saved to: \n",
            "28_May_202006_31PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_31PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['r7sxv2qw']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183135-r7sxv2qw/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183135-r7sxv2qw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183135-r7sxv2qw/wandb-history.jsonl\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  648.033325\n",
            "66  2020-04-22  sub_region  ...             66  696.399780\n",
            "67  2020-04-23  sub_region  ...             67  633.036682\n",
            "68  2020-04-24  sub_region  ...             68  599.434021\n",
            "69  2020-04-25  sub_region  ...             69  679.843323\n",
            "70  2020-04-26  sub_region  ...             70  630.065979\n",
            "71  2020-04-27  sub_region  ...             71  673.871094\n",
            "72  2020-04-28  sub_region  ...             72  681.052856\n",
            "73  2020-04-29  sub_region  ...             73  670.992432\n",
            "74  2020-04-30  sub_region  ...             74  661.747437\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183135-r7sxv2qw/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183135-r7sxv2qw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183135-r7sxv2qw/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183135-r7sxv2qw/media/plotly/test_plot_20_c2722b2a.plotly.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183135-r7sxv2qw/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183135-r7sxv2qw/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: r7sxv2qw \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183135-r7sxv2qw/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183135-r7sxv2qw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183135-r7sxv2qw/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183135-r7sxv2qw/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183135-r7sxv2qw/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183135-r7sxv2qw/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: r7sxv2qw\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: fnjnnh45 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: fnjnnh45\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/fnjnnh45\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/fnjnnh45</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/fnjnnh45\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmZuam5uaDQ1OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "12.417334482073784\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 0\n",
            "0.5173889367530743\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183147-fnjnnh45/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183147-fnjnnh45/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183147-fnjnnh45/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183147-fnjnnh45/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183147-fnjnnh45/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183147-fnjnnh45/media/graph/graph_0_summary_eb031a55.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183147-fnjnnh45/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183147-fnjnnh45/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183147-fnjnnh45/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.187367584556341\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.5911403160231808\n",
            "The running loss is:\n",
            "8.399394869804382\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.34997478624184925\n",
            "1\n",
            "The running loss is:\n",
            "6.990775217302144\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 3\n",
            "0.29128230072092265\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183147-fnjnnh45/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183147-fnjnnh45/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.946204421576113\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 4\n",
            "0.37275851756567135\n",
            "1\n",
            "The running loss is:\n",
            "15.735674642026424\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n",
            "0.6556531100844344\n",
            "The running loss is:\n",
            "7.7431636694818735\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 6\n",
            "0.32263181956174475\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183147-fnjnnh45/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183147-fnjnnh45/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "4.328124134801328\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 7\n",
            "0.18033850561672202\n",
            "2\n",
            "The running loss is:\n",
            "3.735455541405827\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 8\n",
            "0.15564398089190945\n",
            "The running loss is:\n",
            "3.9662495320662856\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 9\n",
            "0.16526039716942856\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183147-fnjnnh45/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183147-fnjnnh45/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "Data saved to: \n",
            "28_May_202006_31PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_31PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['fnjnnh45']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183147-fnjnnh45/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183147-fnjnnh45/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183147-fnjnnh45/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  755.517212\n",
            "66  2020-04-22  sub_region  ...             66  771.916016\n",
            "67  2020-04-23  sub_region  ...             67  760.687378\n",
            "68  2020-04-24  sub_region  ...             68  735.305786\n",
            "69  2020-04-25  sub_region  ...             69  728.676880\n",
            "70  2020-04-26  sub_region  ...             70  742.574097\n",
            "71  2020-04-27  sub_region  ...             71  690.534424\n",
            "72  2020-04-28  sub_region  ...             72  759.479492\n",
            "73  2020-04-29  sub_region  ...             73  761.484863\n",
            "74  2020-04-30  sub_region  ...             74  765.487183\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183147-fnjnnh45/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183147-fnjnnh45/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183147-fnjnnh45/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183147-fnjnnh45/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183147-fnjnnh45/media/plotly/test_plot_20_1d2f2943.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183147-fnjnnh45/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183147-fnjnnh45/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: fnjnnh45 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183147-fnjnnh45/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183147-fnjnnh45/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183147-fnjnnh45/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183147-fnjnnh45/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183147-fnjnnh45/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183147-fnjnnh45/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: fnjnnh45\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 31nb2sbt with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 31nb2sbt\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/31nb2sbt\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/31nb2sbt</a><br/>\n",
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          "metadata": {
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        {
          "output_type": "stream",
          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/31nb2sbt\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjMxbmIyc2J0OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "12.737247206270695\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 0\n",
            "0.5307186335946122\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183204-31nb2sbt/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183204-31nb2sbt/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183204-31nb2sbt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183204-31nb2sbt/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183204-31nb2sbt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183204-31nb2sbt/media/graph/graph_0_summary_a4148c75.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183204-31nb2sbt/requirements.txt\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183204-31nb2sbt/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "12.846482746303082\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183204-31nb2sbt/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.5352701144292951\n",
            "The running loss is:\n",
            "8.131057146936655\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.3387940477890273\n",
            "1\n",
            "The running loss is:\n",
            "5.895240487996489\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 3\n",
            "0.24563502033318704\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183204-31nb2sbt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183204-31nb2sbt/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.398755688220263\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 4\n",
            "0.34994815367584425\n",
            "The running loss is:\n",
            "16.694155018776655\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n",
            "0.6955897924490273\n",
            "The running loss is:\n",
            "9.139933694154024\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 6\n",
            "0.380830570589751\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183204-31nb2sbt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183204-31nb2sbt/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.126268687658012\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 7\n",
            "0.21359452865241715\n",
            "2\n",
            "The running loss is:\n",
            "4.945361414458603\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 8\n",
            "0.2060567256024418\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_32PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183204-31nb2sbt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183204-31nb2sbt/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_32PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  596.721497\n",
            "66  2020-04-22  sub_region  ...             66  611.140503\n",
            "67  2020-04-23  sub_region  ...             67  602.298889\n",
            "68  2020-04-24  sub_region  ...             68  592.272522\n",
            "69  2020-04-25  sub_region  ...             69  590.910889\n",
            "70  2020-04-26  sub_region  ...             70  592.348145\n",
            "71  2020-04-27  sub_region  ...             71  565.893555\n",
            "72  2020-04-28  sub_region  ...             72  597.307617\n",
            "73  2020-04-29  sub_region  ...             73  598.141846\n",
            "74  2020-04-30  sub_region  ...             74  604.241028\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['31nb2sbt']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183204-31nb2sbt/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183204-31nb2sbt/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183204-31nb2sbt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183204-31nb2sbt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183204-31nb2sbt/media/plotly/test_plot_18_b0d51432.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183204-31nb2sbt/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183204-31nb2sbt/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 31nb2sbt \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183204-31nb2sbt/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183204-31nb2sbt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183204-31nb2sbt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183204-31nb2sbt/media/plotly/test_plot_all_19_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183204-31nb2sbt/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183204-31nb2sbt/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 31nb2sbt\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 7wcnck5x with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 7wcnck5x\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
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              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/7wcnck5x\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/7wcnck5x</a><br/>\n",
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          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
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          "output_type": "stream",
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "15.76520562171936\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 0\n",
            "0.65688356757164\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183216-7wcnck5x/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183216-7wcnck5x/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183216-7wcnck5x/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183216-7wcnck5x/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183216-7wcnck5x/requirements.txt\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.477864772081375\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.6449110321700573\n",
            "1\n",
            "The running loss is:\n",
            "12.414291091263294\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.5172621288026372\n",
            "2\n",
            "The running loss is:\n",
            "7.5504014934413135\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 3\n",
            "0.3146000622267214\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183216-7wcnck5x/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183216-7wcnck5x/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.823520310223103\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 4\n",
            "0.5343133462592959\n",
            "1\n",
            "The running loss is:\n",
            "6.759511549025774\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n",
            "0.2816463145427406\n",
            "The running loss is:\n",
            "6.30719225294888\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 6\n",
            "0.26279967720620334\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183216-7wcnck5x/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183216-7wcnck5x/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.85306416079402\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 7\n",
            "0.28554434003308415\n",
            "2\n",
            "The running loss is:\n",
            "6.9310750886797905\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 8\n",
            "0.2887947953616579\n",
            "The running loss is:\n",
            "8.455319091677666\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 9\n",
            "0.3523049621532361\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202006_32PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183216-7wcnck5x/wandb-history.jsonl\n",
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183216-7wcnck5x/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_32PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['7wcnck5x']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  779.131897\n",
            "66  2020-04-22  sub_region  ...             66  792.555298\n",
            "67  2020-04-23  sub_region  ...             67  780.039795\n",
            "68  2020-04-24  sub_region  ...             68  769.559204\n",
            "69  2020-04-25  sub_region  ...             69  768.763855\n",
            "70  2020-04-26  sub_region  ...             70  775.261108\n",
            "71  2020-04-27  sub_region  ...             71  753.575806\n",
            "72  2020-04-28  sub_region  ...             72  784.202087\n",
            "73  2020-04-29  sub_region  ...             73  783.728699\n",
            "74  2020-04-30  sub_region  ...             74  785.233765\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183216-7wcnck5x/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183216-7wcnck5x/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183216-7wcnck5x/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183216-7wcnck5x/wandb-summary.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183216-7wcnck5x/media/plotly/test_plot_20_f7c7c22f.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183216-7wcnck5x/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183216-7wcnck5x/media/plotly\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 7wcnck5x \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183216-7wcnck5x/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183216-7wcnck5x/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183216-7wcnck5x/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183216-7wcnck5x/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183216-7wcnck5x/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183216-7wcnck5x/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 7wcnck5x\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: flfnr7zc with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: flfnr7zc\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/flfnr7zc\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/flfnr7zc</a><br/>\n",
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          "metadata": {
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/flfnr7zc\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmZsZm5yN3pjOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "15.728306412696838\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 0\n",
            "0.655346100529035\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183229-flfnr7zc/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183229-flfnr7zc/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183229-flfnr7zc/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183229-flfnr7zc/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183229-flfnr7zc/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183229-flfnr7zc/media/graph/graph_0_summary_0bf0f215.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183229-flfnr7zc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183229-flfnr7zc/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183229-flfnr7zc/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.534287229180336\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.5639286345491806\n",
            "1\n",
            "The running loss is:\n",
            "10.27218734100461\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.4280078058751921\n",
            "2\n",
            "The running loss is:\n",
            "5.5147014474496245\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 3\n",
            "0.22977922697706768\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183229-flfnr7zc/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183229-flfnr7zc/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.376355066895485\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 4\n",
            "0.3906814611206452\n",
            "1\n",
            "The running loss is:\n",
            "6.485733915120363\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n",
            "0.27023891313001513\n",
            "2\n",
            "The running loss is:\n",
            "7.979923363775015\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 6\n",
            "0.33249680682395893\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183229-flfnr7zc/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183229-flfnr7zc/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.143040359020233\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 7\n",
            "0.33929334829250973\n",
            "1\n",
            "The running loss is:\n",
            "5.536451553925872\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 8\n",
            "0.230685481413578\n",
            "2\n",
            "The running loss is:\n",
            "4.550636069383472\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 9\n",
            "0.18960983622431135\n",
            "Data saved to: \n",
            "28_May_202006_32PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183229-flfnr7zc/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183229-flfnr7zc/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_32PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['flfnr7zc']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  756.924194\n",
            "66  2020-04-22  sub_region  ...             66  788.874878\n",
            "67  2020-04-23  sub_region  ...             67  767.624146\n",
            "68  2020-04-24  sub_region  ...             68  745.193481\n",
            "69  2020-04-25  sub_region  ...             69  751.710815\n",
            "70  2020-04-26  sub_region  ...             70  755.871094\n",
            "71  2020-04-27  sub_region  ...             71  710.098877\n",
            "72  2020-04-28  sub_region  ...             72  772.029785\n",
            "73  2020-04-29  sub_region  ...             73  776.352173\n",
            "74  2020-04-30  sub_region  ...             74  783.972778\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183229-flfnr7zc/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183229-flfnr7zc/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183229-flfnr7zc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183229-flfnr7zc/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183229-flfnr7zc/media/plotly/test_plot_20_75431aa1.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183229-flfnr7zc/media\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183229-flfnr7zc/media/plotly\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: flfnr7zc \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183229-flfnr7zc/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183229-flfnr7zc/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183229-flfnr7zc/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183229-flfnr7zc/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183229-flfnr7zc/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183229-flfnr7zc/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: flfnr7zc\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: kiy0qx6s with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: kiy0qx6s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/kiy0qx6s\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/kiy0qx6s</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/kiy0qx6s\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmtpeTBxeDZzOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "12.287862300872803\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 0\n",
            "0.5342548826466436\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183242-kiy0qx6s/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183242-kiy0qx6s/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183242-kiy0qx6s/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183242-kiy0qx6s/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183242-kiy0qx6s/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183242-kiy0qx6s/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183242-kiy0qx6s/media/graph/graph_0_summary_eb18aaf4.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183242-kiy0qx6s/media/graph\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.926748000085354\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 1\n",
            "0.6055107826124067\n",
            "1\n",
            "The running loss is:\n",
            "11.354672580957413\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 2\n",
            "0.4936814165633658\n",
            "2\n",
            "The running loss is:\n",
            "9.702557804062963\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 3\n",
            "0.4218503393070853\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183242-kiy0qx6s/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183242-kiy0qx6s/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.627340912818909\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 4\n",
            "0.5055365614269091\n",
            "1\n",
            "The running loss is:\n",
            "6.340536650270224\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 5\n",
            "0.275675506533488\n",
            "2\n",
            "The running loss is:\n",
            "10.078678345307708\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 6\n",
            "0.4382034063177264\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183242-kiy0qx6s/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183242-kiy0qx6s/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.461791813373566\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 7\n",
            "0.3679039918858072\n",
            "1\n",
            "The running loss is:\n",
            "4.9823848977684975\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 8\n",
            "0.21662543033776077\n",
            "2\n",
            "The running loss is:\n",
            "3.317102179862559\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 9\n",
            "0.14422183390706778\n",
            "Data saved to: \n",
            "28_May_202006_32PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183242-kiy0qx6s/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183242-kiy0qx6s/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_32PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['kiy0qx6s']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  835.124390\n",
            "66  2020-04-22  sub_region  ...             66  840.772278\n",
            "67  2020-04-23  sub_region  ...             67  832.618164\n",
            "68  2020-04-24  sub_region  ...             68  835.653015\n",
            "69  2020-04-25  sub_region  ...             69  821.924072\n",
            "70  2020-04-26  sub_region  ...             70  833.929749\n",
            "71  2020-04-27  sub_region  ...             71  823.226929\n",
            "72  2020-04-28  sub_region  ...             72  831.859680\n",
            "73  2020-04-29  sub_region  ...             73  828.141541\n",
            "74  2020-04-30  sub_region  ...             74  831.716797\n",
            "\n",
            "[25 rows x 28 columns]"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183242-kiy0qx6s/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
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          "name": "stdout"
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          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183242-kiy0qx6s/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183242-kiy0qx6s/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183242-kiy0qx6s/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183242-kiy0qx6s/media/plotly/test_plot_20_7a7a1d5c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183242-kiy0qx6s/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183242-kiy0qx6s/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: kiy0qx6s \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183242-kiy0qx6s/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183242-kiy0qx6s/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183242-kiy0qx6s/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183242-kiy0qx6s/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183242-kiy0qx6s/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183242-kiy0qx6s/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: kiy0qx6s\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: xhheep9s with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 1\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: xhheep9s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/xhheep9s\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/xhheep9s</a><br/>\n",
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          "metadata": {
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        {
          "output_type": "stream",
          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/xhheep9s\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnhoaGVlcDlzOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 1\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 1\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n",
            "The running loss is:\n",
            "12.292471200227737\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 0\n",
            "0.534455269575119\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183254-xhheep9s/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183254-xhheep9s/media/graph/graph_0_summary_59344234.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183254-xhheep9s/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183254-xhheep9s/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183254-xhheep9s/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183254-xhheep9s/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183254-xhheep9s/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183254-xhheep9s/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183254-xhheep9s/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.315681867301464\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 1\n",
            "0.6224209507522376\n",
            "1\n",
            "The running loss is:\n",
            "10.289977289736271\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 2\n",
            "0.44739031694505527\n",
            "2\n",
            "The running loss is:\n",
            "7.302962388843298\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 3\n",
            "0.3175201038627521\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183254-xhheep9s/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183254-xhheep9s/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.639127224683762\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 4\n",
            "0.462570748899294\n",
            "The running loss is:\n",
            "5.083698073402047\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 5\n",
            "0.2210303510174803\n",
            "1\n",
            "The running loss is:\n",
            "4.266114007681608\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 6\n",
            "0.1854832177252873\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183254-xhheep9s/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183254-xhheep9s/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "3.362240646034479\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 7\n",
            "0.14618437591454256\n",
            "The running loss is:\n",
            "5.346718622371554\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 8\n",
            "0.2324660270596328\n",
            "1\n",
            "The running loss is:\n",
            "3.976900838315487\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 9\n",
            "0.17290873210067334\n",
            "Data saved to: \n",
            "28_May_202006_32PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183254-xhheep9s/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183254-xhheep9s/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_32PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['xhheep9s']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  773.226318\n",
            "66  2020-04-22  sub_region  ...             66  785.933838\n",
            "67  2020-04-23  sub_region  ...             67  776.867065\n",
            "68  2020-04-24  sub_region  ...             68  779.012268\n",
            "69  2020-04-25  sub_region  ...             69  770.271240\n",
            "70  2020-04-26  sub_region  ...             70  779.572510\n",
            "71  2020-04-27  sub_region  ...             71  765.994873\n",
            "72  2020-04-28  sub_region  ...             72  776.860962\n",
            "73  2020-04-29  sub_region  ...             73  774.669739\n",
            "74  2020-04-30  sub_region  ...             74  777.345093\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183254-xhheep9s/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183254-xhheep9s/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183254-xhheep9s/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183254-xhheep9s/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183254-xhheep9s/media/plotly/test_plot_20_b982c2b4.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183254-xhheep9s/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: xhheep9s \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183254-xhheep9s/media/plotly\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183254-xhheep9s/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183254-xhheep9s/wandb-metadata.json\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183254-xhheep9s/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: xhheep9s\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
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            "wandb: Agent Starting Run: eqtdfgtw with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: eqtdfgtw\n"
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          "name": "stdout"
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          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/eqtdfgtw\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/eqtdfgtw</a><br/>\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/eqtdfgtw\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
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          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/media/graph/graph_0_summary_7174f2ec.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.145409000921063\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.4058163600368425\n",
            "The running loss is:\n",
            "25.701497849076986\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "1.0280599139630795\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.272816001903266\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.8109126400761306\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.751347083598375\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.790053883343935\n",
            "The running loss is:\n",
            "18.793059289455414\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.7517223715782165\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.612464647740126\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.664498585909605\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['eqtdfgtw']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.692850414896384\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.38771401659585536\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.73813858255744\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.7895255433022976\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.20803095260635\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.488321238104254\n",
            "1\n",
            "The running loss is:\n",
            "11.476102620712481\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.4590441048284993\n",
            "2\n",
            "Data saved to: \n",
            "28_May_202006_33PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_33PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  801.515869\n",
            "66  2020-04-22  sub_region  ...             66  801.592773\n",
            "67  2020-04-23  sub_region  ...             67  801.521545\n",
            "68  2020-04-24  sub_region  ...             68  801.597168\n",
            "69  2020-04-25  sub_region  ...             69  801.738037\n",
            "70  2020-04-26  sub_region  ...             70  801.605835\n",
            "71  2020-04-27  sub_region  ...             71  801.837097\n",
            "72  2020-04-28  sub_region  ...             72  801.528931\n",
            "73  2020-04-29  sub_region  ...             73  801.504639\n",
            "74  2020-04-30  sub_region  ...             74  801.555054\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/media/plotly/test_plot_20_fcc89d57.plotly.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/media/plotly\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: eqtdfgtw \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183306-eqtdfgtw/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: eqtdfgtw\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: xmhc8vgh with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: xmhc8vgh\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/xmhc8vgh\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/xmhc8vgh</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/xmhc8vgh\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnhtaGM4dmdoOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183323-xmhc8vgh/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183323-xmhc8vgh/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183323-xmhc8vgh/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183323-xmhc8vgh/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183323-xmhc8vgh/media/graph/graph_0_summary_7b56b8de.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183323-xmhc8vgh/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183323-xmhc8vgh/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183323-xmhc8vgh/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183323-xmhc8vgh/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.096708084311103\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.40386832337244416\n",
            "The running loss is:\n",
            "25.173049704171717\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "1.0069219881668687\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183323-xmhc8vgh/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183323-xmhc8vgh/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.567884587217122\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.8227153834886849\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183323-xmhc8vgh/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183323-xmhc8vgh/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.932578061707318\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.6773031224682927\n",
            "1\n",
            "The running loss is:\n",
            "11.519542964175344\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.4607817185670137\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183323-xmhc8vgh/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183323-xmhc8vgh/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.808077305089682\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.3523230922035873\n",
            "The running loss is:\n",
            "11.38830050965771\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.4555320203863084\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['xmhc8vgh']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183323-xmhc8vgh/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183323-xmhc8vgh/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.139500390738249\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.28558001562952995\n",
            "2\n",
            "The running loss is:\n",
            "9.381748400162905\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.3752699360065162\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183323-xmhc8vgh/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183323-xmhc8vgh/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.521757567301393\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.4208703026920557\n",
            "Data saved to: \n",
            "28_May_202006_33PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183323-xmhc8vgh/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183323-xmhc8vgh/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_33PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  761.296265\n",
            "66  2020-04-22  sub_region  ...             66  761.453735\n",
            "67  2020-04-23  sub_region  ...             67  761.306885\n",
            "68  2020-04-24  sub_region  ...             68  761.469482\n",
            "69  2020-04-25  sub_region  ...             69  761.735107\n",
            "70  2020-04-26  sub_region  ...             70  761.236694\n",
            "71  2020-04-27  sub_region  ...             71  761.615662\n",
            "72  2020-04-28  sub_region  ...             72  760.781616\n",
            "73  2020-04-29  sub_region  ...             73  760.612366\n",
            "74  2020-04-30  sub_region  ...             74  760.868896\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183323-xmhc8vgh/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183323-xmhc8vgh/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: xmhc8vgh \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183323-xmhc8vgh/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183323-xmhc8vgh/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183323-xmhc8vgh/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183323-xmhc8vgh/media/plotly/test_plot_20_b3c2d6a3.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183323-xmhc8vgh/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183323-xmhc8vgh/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183323-xmhc8vgh/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: xmhc8vgh\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 6gbpik86 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 6gbpik86\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/6gbpik86\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/6gbpik86</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/6gbpik86\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjZnYnBpazg2OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
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          ],
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        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183341-6gbpik86/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183341-6gbpik86/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183341-6gbpik86/media/graph/graph_0_summary_ed06505c.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183341-6gbpik86/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183341-6gbpik86/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183341-6gbpik86/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183341-6gbpik86/wandb-metadata.json\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183341-6gbpik86/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.584014743566513\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.8233605897426606\n",
            "The running loss is:\n",
            "17.932957649230957\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.7173183059692383\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183341-6gbpik86/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183341-6gbpik86/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.548911690013483\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.6219564676005394\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183341-6gbpik86/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183341-6gbpik86/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.730191620066762\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.5892076648026705\n",
            "The running loss is:\n",
            "15.559959415346384\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.6223983766138553\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183341-6gbpik86/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183341-6gbpik86/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.396017402410507\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.4558406960964203\n",
            "2\n",
            "The running loss is:\n",
            "9.876246387138963\n",
            "The number of items in train is: \n",
            "25\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['6gbpik86']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The loss for epoch 6\n",
            "0.3950498554855585\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_33PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183341-6gbpik86/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183341-6gbpik86/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_33PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  505.433014\n",
            "66  2020-04-22  sub_region  ...             66  504.062500\n",
            "67  2020-04-23  sub_region  ...             67  504.517761\n",
            "68  2020-04-24  sub_region  ...             68  503.535156\n",
            "69  2020-04-25  sub_region  ...             69  506.940369\n",
            "70  2020-04-26  sub_region  ...             70  505.692200\n",
            "71  2020-04-27  sub_region  ...             71  506.551117\n",
            "72  2020-04-28  sub_region  ...             72  507.229309\n",
            "73  2020-04-29  sub_region  ...             73  507.427246\n",
            "74  2020-04-30  sub_region  ...             74  507.579987\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183341-6gbpik86/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183341-6gbpik86/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 6gbpik86 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183341-6gbpik86/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183341-6gbpik86/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183341-6gbpik86/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183341-6gbpik86/media/plotly/test_plot_all_15_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183341-6gbpik86/media/plotly/test_plot_14_c7166984.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183341-6gbpik86/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183341-6gbpik86/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183341-6gbpik86/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 6gbpik86\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: hb8ltgdq with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: hb8ltgdq\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/hb8ltgdq\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/hb8ltgdq</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/hb8ltgdq\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmhiOGx0Z2RxOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183354-hb8ltgdq/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183354-hb8ltgdq/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183354-hb8ltgdq/media/graph/graph_0_summary_2c5aade9.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183354-hb8ltgdq/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183354-hb8ltgdq/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183354-hb8ltgdq/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183354-hb8ltgdq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183354-hb8ltgdq/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183354-hb8ltgdq/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.51875665783882\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.8207502663135529\n",
            "The running loss is:\n",
            "18.745911203324795\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.7498364481329918\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183354-hb8ltgdq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183354-hb8ltgdq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.171599298715591\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.5668639719486237\n",
            "2\n",
            "The running loss is:\n",
            "10.58555408520624\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.4234221634082496\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183354-hb8ltgdq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183354-hb8ltgdq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.37247646227479\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.6948990584909915\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183354-hb8ltgdq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183354-hb8ltgdq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.633864669129252\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.4253545867651701\n",
            "1\n",
            "The running loss is:\n",
            "9.219570185989141\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.36878280743956565\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['hb8ltgdq']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183354-hb8ltgdq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183354-hb8ltgdq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.460247997194529\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.33840991988778113\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_33PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_33PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183354-hb8ltgdq/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183354-hb8ltgdq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183354-hb8ltgdq/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  684.219727\n",
            "66  2020-04-22  sub_region  ...             66  684.323364\n",
            "67  2020-04-23  sub_region  ...             67  684.080750\n",
            "68  2020-04-24  sub_region  ...             68  683.885254\n",
            "69  2020-04-25  sub_region  ...             69  685.422363\n",
            "70  2020-04-26  sub_region  ...             70  684.204346\n",
            "71  2020-04-27  sub_region  ...             71  685.634094\n",
            "72  2020-04-28  sub_region  ...             72  684.494141\n",
            "73  2020-04-29  sub_region  ...             73  684.385315\n",
            "74  2020-04-30  sub_region  ...             74  684.536133\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183354-hb8ltgdq/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183354-hb8ltgdq/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183354-hb8ltgdq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183354-hb8ltgdq/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183354-hb8ltgdq/media/plotly/test_plot_16_e1926f30.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183354-hb8ltgdq/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183354-hb8ltgdq/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: hb8ltgdq \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183354-hb8ltgdq/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183354-hb8ltgdq/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183354-hb8ltgdq/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183354-hb8ltgdq/media/plotly/test_plot_all_17_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183354-hb8ltgdq/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183354-hb8ltgdq/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: hb8ltgdq\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: xnyz7w5q with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: xnyz7w5q\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/xnyz7w5q\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/xnyz7w5q</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/xnyz7w5q\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnhueXo3dzVxOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.772414907813072\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 0\n",
            "0.7821839544922113\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/media/graph/graph_0_summary_f646221f.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/media/graph\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.575059436261654\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.8156274765109023\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.316046819090843\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.6381686174621185\n",
            "The running loss is:\n",
            "11.947806634008884\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 3\n",
            "0.49782527641703683\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.35117731243372\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 4\n",
            "0.43129905468473834\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.100783228874207\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n",
            "0.4625326345364253\n",
            "2\n",
            "The running loss is:\n",
            "11.392054236494005\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 6\n",
            "0.4746689265205835\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['xnyz7w5q']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.173449165187776\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 7\n",
            "0.215560381882824\n",
            "1\n",
            "The running loss is:\n",
            "9.032942300196737\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 8\n",
            "0.3763725958415307\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.382783502340317\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 9\n",
            "0.8076159792641798\n",
            "Data saved to: \n",
            "28_May_202006_34PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_34PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  515.919434\n",
            "66  2020-04-22  sub_region  ...             66  516.543030\n",
            "67  2020-04-23  sub_region  ...             67  515.078186\n",
            "68  2020-04-24  sub_region  ...             68  516.592896\n",
            "69  2020-04-25  sub_region  ...             69  515.011414\n",
            "70  2020-04-26  sub_region  ...             70  514.803894\n",
            "71  2020-04-27  sub_region  ...             71  516.728943\n",
            "72  2020-04-28  sub_region  ...             72  515.620483\n",
            "73  2020-04-29  sub_region  ...             73  515.851013\n",
            "74  2020-04-30  sub_region  ...             74  514.158508\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/media/plotly/test_plot_20_14ad17d9.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: xnyz7w5q \n",
            "\n"
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          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183412-xnyz7w5q/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: xnyz7w5q\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: p15mr0mp with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: p15mr0mp\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/p15mr0mp\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/p15mr0mp</a><br/>\n",
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/p15mr0mp\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnAxNW1yMG1wOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183429-p15mr0mp/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183429-p15mr0mp/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183429-p15mr0mp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183429-p15mr0mp/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183429-p15mr0mp/media/graph/graph_0_summary_c73f39c4.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183429-p15mr0mp/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183429-p15mr0mp/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183429-p15mr0mp/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183429-p15mr0mp/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.797405660152435\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 0\n",
            "0.7832252358396848\n",
            "The running loss is:\n",
            "19.402401946485043\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.8084334144368768\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183429-p15mr0mp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183429-p15mr0mp/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.805835217237473\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.616909800718228\n",
            "The running loss is:\n",
            "9.891540326178074\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 3\n",
            "0.4121475135907531\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183429-p15mr0mp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183429-p15mr0mp/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "5.6078918017446995\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 4\n",
            "0.23366215840602914\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183429-p15mr0mp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183429-p15mr0mp/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "4.041457449435256\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n",
            "0.16839406039313567\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_34PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_34PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['p15mr0mp']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183429-p15mr0mp/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183429-p15mr0mp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183429-p15mr0mp/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  378.001099\n",
            "66  2020-04-22  sub_region  ...             66  379.296204\n",
            "67  2020-04-23  sub_region  ...             67  378.866272\n",
            "68  2020-04-24  sub_region  ...             68  383.898071\n",
            "69  2020-04-25  sub_region  ...             69  369.884155\n",
            "70  2020-04-26  sub_region  ...             70  381.321381\n",
            "71  2020-04-27  sub_region  ...             71  371.926025\n",
            "72  2020-04-28  sub_region  ...             72  378.183258\n",
            "73  2020-04-29  sub_region  ...             73  379.500549\n",
            "74  2020-04-30  sub_region  ...             74  378.237946\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183429-p15mr0mp/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183429-p15mr0mp/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183429-p15mr0mp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183429-p15mr0mp/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183429-p15mr0mp/media/plotly/test_plot_12_9498e4f6.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183429-p15mr0mp/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183429-p15mr0mp/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: p15mr0mp \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183429-p15mr0mp/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183429-p15mr0mp/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183429-p15mr0mp/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183429-p15mr0mp/media/plotly/test_plot_all_13_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183429-p15mr0mp/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183429-p15mr0mp/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: p15mr0mp\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 4674polk with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 4674polk\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/4674polk\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/4674polk</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/4674polk\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjQ2NzRwb2xrOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183441-4674polk/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183441-4674polk/media/graph/graph_0_summary_6c7dd0eb.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183441-4674polk/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183441-4674polk/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183441-4674polk/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183441-4674polk/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183441-4674polk/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183441-4674polk/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183441-4674polk/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.451373293995857\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 0\n",
            "0.8938072205831608\n",
            "The running loss is:\n",
            "18.959119454026222\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.7899633105844259\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183441-4674polk/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183441-4674polk/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.811535373330116\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.7004806405554215\n",
            "The running loss is:\n",
            "15.114799819886684\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 3\n",
            "0.6297833258286119\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183441-4674polk/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183441-4674polk/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.837378360331059\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 4\n",
            "0.5765574316804608\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183441-4674polk/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183441-4674polk/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.938715167343616\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n",
            "0.45577979863931734\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_34PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_34PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['4674polk']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183441-4674polk/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183441-4674polk/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183441-4674polk/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  379.669922\n",
            "66  2020-04-22  sub_region  ...             66  381.567261\n",
            "67  2020-04-23  sub_region  ...             67  381.375244\n",
            "68  2020-04-24  sub_region  ...             68  386.184875\n",
            "69  2020-04-25  sub_region  ...             69  368.961060\n",
            "70  2020-04-26  sub_region  ...             70  383.574829\n",
            "71  2020-04-27  sub_region  ...             71  370.266907\n",
            "72  2020-04-28  sub_region  ...             72  379.182159\n",
            "73  2020-04-29  sub_region  ...             73  380.702667\n",
            "74  2020-04-30  sub_region  ...             74  380.433716\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183441-4674polk/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183441-4674polk/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183441-4674polk/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183441-4674polk/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183441-4674polk/media/plotly/test_plot_12_ab9062b5.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183441-4674polk/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183441-4674polk/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 4674polk \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183441-4674polk/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183441-4674polk/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183441-4674polk/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183441-4674polk/media/plotly/test_plot_all_13_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183441-4674polk/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183441-4674polk/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 4674polk\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: m35p2vlx with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: m35p2vlx\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/m35p2vlx\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/m35p2vlx</a><br/>\n",
              "            "
            ],
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              "<IPython.core.display.HTML object>"
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          "metadata": {
            "tags": []
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/m35p2vlx\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOm0zNXAydmx4OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183459-m35p2vlx/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183459-m35p2vlx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183459-m35p2vlx/media/graph/graph_0_summary_3088cdae.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183459-m35p2vlx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183459-m35p2vlx/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183459-m35p2vlx/wandb-events.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.45643301308155\n",
            "The number of items in train is: \n",
            "24\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183459-m35p2vlx/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183459-m35p2vlx/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The loss for epoch 0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183459-m35p2vlx/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0.8940180422117313\n",
            "The running loss is:\n",
            "18.878746390342712\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.7866144329309464\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183459-m35p2vlx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183459-m35p2vlx/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.909177042543888\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.7045490434393287\n",
            "The running loss is:\n",
            "14.992017075419426\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 3\n",
            "0.6246673781424761\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183459-m35p2vlx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183459-m35p2vlx/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.057133480906487\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 4\n",
            "0.5440472283711036\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183459-m35p2vlx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183459-m35p2vlx/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "7.168162252753973\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n",
            "0.2986734271980822\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_35PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_35PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['m35p2vlx']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  381.720642\n",
            "66  2020-04-22  sub_region  ...             66  383.487915\n",
            "67  2020-04-23  sub_region  ...             67  382.618774\n",
            "68  2020-04-24  sub_region  ...             68  386.354279\n",
            "69  2020-04-25  sub_region  ...             69  371.824524\n",
            "70  2020-04-26  sub_region  ...             70  384.303436\n",
            "71  2020-04-27  sub_region  ...             71  373.255554\n",
            "72  2020-04-28  sub_region  ...             72  381.710022\n",
            "73  2020-04-29  sub_region  ...             73  383.322937\n",
            "74  2020-04-30  sub_region  ...             74  382.421906\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183459-m35p2vlx/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183459-m35p2vlx/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183459-m35p2vlx/wandb-history.jsonl\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183459-m35p2vlx/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: m35p2vlx \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183459-m35p2vlx/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183459-m35p2vlx/media/plotly/test_plot_all_13_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183459-m35p2vlx/media/plotly/test_plot_12_d4e101e3.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183459-m35p2vlx/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183459-m35p2vlx/media/plotly\n",
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183459-m35p2vlx/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183459-m35p2vlx/wandb-events.jsonl\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: m35p2vlx\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 0czseyt9 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 0czseyt9\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/0czseyt9\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/0czseyt9</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/0czseyt9\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjBjenNleXQ5OmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183511-0czseyt9/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183511-0czseyt9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183511-0czseyt9/media/graph/graph_0_summary_8186c48a.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183511-0czseyt9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183511-0czseyt9/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183511-0czseyt9/wandb-events.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183511-0czseyt9/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183511-0czseyt9/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183511-0czseyt9/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "17.729351297020912\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 0\n",
            "0.7708413607400396\n",
            "The running loss is:\n",
            "17.55451713502407\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 1\n",
            "0.7632398754358292\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183511-0czseyt9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183511-0czseyt9/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.769226357340813\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 2\n",
            "0.5986620155365571\n",
            "1\n",
            "The running loss is:\n",
            "10.689910009503365\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 3\n",
            "0.46477869606536365\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183511-0czseyt9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183511-0czseyt9/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.719441205263138\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 4\n",
            "0.4660626610983973\n",
            "The running loss is:\n",
            "11.102143481373787\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 5\n",
            "0.48270189049451245\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183511-0czseyt9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183511-0czseyt9/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.552160829305649\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 6\n",
            "0.3718330795350282\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['0czseyt9']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183511-0czseyt9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183511-0czseyt9/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "8.911982571706176\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 7\n",
            "0.3874775031176598\n",
            "1\n",
            "The running loss is:\n",
            "10.839222326874733\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 8\n",
            "0.4712705359510753\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183511-0czseyt9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183511-0czseyt9/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "6.471042025834322\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 9\n",
            "0.28134965329714445\n",
            "Data saved to: \n",
            "28_May_202006_35PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_35PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183511-0czseyt9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183511-0czseyt9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183511-0czseyt9/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  682.042175\n",
            "66  2020-04-22  sub_region  ...             66  684.554626\n",
            "67  2020-04-23  sub_region  ...             67  676.001160\n",
            "68  2020-04-24  sub_region  ...             68  688.336792\n",
            "69  2020-04-25  sub_region  ...             69  666.439087\n",
            "70  2020-04-26  sub_region  ...             70  671.417664\n",
            "71  2020-04-27  sub_region  ...             71  689.934692\n",
            "72  2020-04-28  sub_region  ...             72  674.941956\n",
            "73  2020-04-29  sub_region  ...             73  665.878967\n",
            "74  2020-04-30  sub_region  ...             74  658.025818\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183511-0czseyt9/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183511-0czseyt9/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183511-0czseyt9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183511-0czseyt9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183511-0czseyt9/media/plotly/test_plot_20_790f76c3.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183511-0czseyt9/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183511-0czseyt9/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 0czseyt9 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183511-0czseyt9/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183511-0czseyt9/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183511-0czseyt9/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183511-0czseyt9/wandb-metadata.json\n",
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            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183511-0czseyt9/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 0czseyt9\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
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            "wandb: Agent Starting Run: 4usmwcfe with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 2\n",
            "\tout_seq_length: 5\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: 4usmwcfe\n"
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          "name": "stdout"
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          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/4usmwcfe\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/4usmwcfe</a><br/>\n",
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        {
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          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 5\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 5\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 2\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 5\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 2\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 5\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183528-4usmwcfe/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.749317228794098\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183528-4usmwcfe/requirements.txt\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0.7717094447301782\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183528-4usmwcfe/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183528-4usmwcfe/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183528-4usmwcfe/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183528-4usmwcfe/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183528-4usmwcfe/media/graph/graph_0_summary_df2ed202.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183528-4usmwcfe/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183528-4usmwcfe/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "17.551174700260162\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 1\n",
            "0.7630945521852245\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183528-4usmwcfe/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183528-4usmwcfe/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.54475101083517\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 2\n",
            "0.5889022178623987\n",
            "1\n",
            "The running loss is:\n",
            "10.344177260994911\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 3\n",
            "0.44974683743456134\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183528-4usmwcfe/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183528-4usmwcfe/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "9.028607338666916\n",
            "The number of items in train is: \n",
            "23\n",
            "The loss for epoch 4\n",
            "0.39254814515943115\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_35PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_35PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183528-4usmwcfe/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183528-4usmwcfe/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183528-4usmwcfe/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  391.728210\n",
            "66  2020-04-22  sub_region  ...             66  392.369507\n",
            "67  2020-04-23  sub_region  ...             67  392.830872\n",
            "68  2020-04-24  sub_region  ...             68  397.064850\n",
            "69  2020-04-25  sub_region  ...             69  385.320221\n",
            "70  2020-04-26  sub_region  ...             70  393.841858\n",
            "71  2020-04-27  sub_region  ...             71  388.418610\n",
            "72  2020-04-28  sub_region  ...             72  390.187714\n",
            "73  2020-04-29  sub_region  ...             73  390.757416\n",
            "74  2020-04-30  sub_region  ...             74  391.191467\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['4usmwcfe']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183528-4usmwcfe/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183528-4usmwcfe/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183528-4usmwcfe/wandb-summary.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183528-4usmwcfe/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183528-4usmwcfe/media/plotly/test_plot_10_ca77e0e0.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183528-4usmwcfe/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183528-4usmwcfe/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 4usmwcfe \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183528-4usmwcfe/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183528-4usmwcfe/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183528-4usmwcfe/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183528-4usmwcfe/media/plotly/test_plot_all_11_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183528-4usmwcfe/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183528-4usmwcfe/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 4usmwcfe\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: s7oyw8gr with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: s7oyw8gr\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/s7oyw8gr\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/s7oyw8gr</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/s7oyw8gr\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnM3b3l3OGdyOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/media/graph/graph_0_summary_9203cfa0.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/requirements.txt\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.765136603731662\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.4306054641492665\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "26.137935783714056\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "1.0455174313485622\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.011536345118657\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.8804614538047463\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.903898189775646\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.7561559275910258\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['s7oyw8gr']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.865029871463776\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.754601194858551\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.70023591836798\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.7880094367347192\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.773742999881506\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.7509497199952603\n",
            "The running loss is:\n",
            "19.47792062163353\n",
            "The number of items in train is: \n",
            "25\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The loss for epoch 7\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0.7791168248653412\n",
            "The running loss is:\n",
            "19.640535548329353\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.7856214219331741\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.046687852591276\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.7618675141036511\n",
            "Data saved to: \n",
            "28_May_202006_35PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_35PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  496.016388\n",
            "66  2020-04-22  sub_region  ...             66  496.046783\n",
            "67  2020-04-23  sub_region  ...             67  495.631775\n",
            "68  2020-04-24  sub_region  ...             68  495.853607\n",
            "69  2020-04-25  sub_region  ...             69  496.806030\n",
            "70  2020-04-26  sub_region  ...             70  495.802399\n",
            "71  2020-04-27  sub_region  ...             71  497.801544\n",
            "72  2020-04-28  sub_region  ...             72  496.134644\n",
            "73  2020-04-29  sub_region  ...             73  495.858582\n",
            "74  2020-04-30  sub_region  ...             74  495.841339\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/media/plotly/test_plot_20_59b2352c.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/media\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/media/plotly\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: s7oyw8gr \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183541-s7oyw8gr/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: s7oyw8gr\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: e51i13de with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 1\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: e51i13de\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/e51i13de\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/e51i13de</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/e51i13de\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmU1MWkxM2RlOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 1\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 1\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 1\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 1\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/media/graph/graph_0_summary_567e9911.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/wandb-history.jsonl\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/media\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.082796600530855\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.44331186402123424\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "26.767839308828115\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "1.0707135723531247\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.91911711357534\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.7967646845430135\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.481270607560873\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.7392508243024349\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['e51i13de']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.93701122701168\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.7574804490804672\n",
            "The running loss is:\n",
            "19.590259796008468\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.7836103918403388\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "18.897615873254836\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.7559046349301934\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.6400615721941\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 7\n",
            "0.785602462887764\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.225644499063492\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 8\n",
            "0.7690257799625396\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.104803455993533\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 9\n",
            "0.7641921382397413\n",
            "2\n",
            "Data saved to: \n",
            "28_May_202006_36PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_36PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  495.774384\n",
            "66  2020-04-22  sub_region  ...             66  495.760284\n",
            "67  2020-04-23  sub_region  ...             67  495.068512\n",
            "68  2020-04-24  sub_region  ...             68  496.070099\n",
            "69  2020-04-25  sub_region  ...             69  497.589020\n",
            "70  2020-04-26  sub_region  ...             70  495.803619\n",
            "71  2020-04-27  sub_region  ...             71  500.093933\n",
            "72  2020-04-28  sub_region  ...             72  495.899109\n",
            "73  2020-04-29  sub_region  ...             73  495.294128\n",
            "74  2020-04-30  sub_region  ...             74  495.290405\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/wandb-summary.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/media/plotly/test_plot_20_b4b150f6.plotly.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: e51i13de \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183603-e51i13de/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: e51i13de\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: b702zpgo with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: b702zpgo\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/b702zpgo\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/b702zpgo</a><br/>\n",
              "            "
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/b702zpgo\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmI3MDJ6cGdvOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
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            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183626-b702zpgo/config.yaml\n",
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            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183626-b702zpgo/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183626-b702zpgo/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183626-b702zpgo/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183626-b702zpgo/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183626-b702zpgo/requirements.txt\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.70665431022644\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.9082661724090576\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183626-b702zpgo/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183626-b702zpgo/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.07255493104458\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.8029021972417831\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183626-b702zpgo/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183626-b702zpgo/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.82785291969776\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.7931141167879104\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183626-b702zpgo/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183626-b702zpgo/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "21.11555242538452\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.8446220970153808\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['b702zpgo']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.238652385771275\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.769546095430851\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183626-b702zpgo/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183626-b702zpgo/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "19.169510439038277\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.766780417561531\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183626-b702zpgo/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183626-b702zpgo/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.454397417604923\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.778175896704197\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_36PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183626-b702zpgo/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183626-b702zpgo/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_36PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  386.295502\n",
            "66  2020-04-22  sub_region  ...             66  386.368958\n",
            "67  2020-04-23  sub_region  ...             67  386.170776\n",
            "68  2020-04-24  sub_region  ...             68  386.041290\n",
            "69  2020-04-25  sub_region  ...             69  386.396393\n",
            "70  2020-04-26  sub_region  ...             70  386.061401\n",
            "71  2020-04-27  sub_region  ...             71  386.280853\n",
            "72  2020-04-28  sub_region  ...             72  386.269623\n",
            "73  2020-04-29  sub_region  ...             73  386.305389\n",
            "74  2020-04-30  sub_region  ...             74  386.221985\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183626-b702zpgo/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183626-b702zpgo/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
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            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: b702zpgo \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183626-b702zpgo/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183626-b702zpgo/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183626-b702zpgo/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183626-b702zpgo/media/plotly/test_plot_all_15_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183626-b702zpgo/media/plotly/test_plot_14_416120dd.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183626-b702zpgo/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183626-b702zpgo/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183626-b702zpgo/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: b702zpgo\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: vid0ihm1 with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 2\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: vid0ihm1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/vid0ihm1\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/vid0ihm1</a><br/>\n",
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          "text": [
            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/vid0ihm1\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnZpZDBpaG0xOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 2\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 2\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 2\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 2\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183644-vid0ihm1/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183644-vid0ihm1/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183644-vid0ihm1/media/graph/graph_0_summary_cd981548.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183644-vid0ihm1/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183644-vid0ihm1/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183644-vid0ihm1/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183644-vid0ihm1/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183644-vid0ihm1/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183644-vid0ihm1/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.725555673241615\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 0\n",
            "0.9090222269296646\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183644-vid0ihm1/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183644-vid0ihm1/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.579730972647667\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 1\n",
            "0.8231892389059067\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183644-vid0ihm1/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183644-vid0ihm1/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.793140269815922\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 2\n",
            "0.7917256107926369\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183644-vid0ihm1/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183644-vid0ihm1/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "20.604701928794384\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 3\n",
            "0.8241880771517753\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['vid0ihm1']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.242981515824795\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 4\n",
            "0.7697192606329918\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183644-vid0ihm1/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183644-vid0ihm1/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "The running loss is:\n",
            "19.15913762152195\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 5\n",
            "0.766365504860878\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183644-vid0ihm1/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183644-vid0ihm1/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.610484287142754\n",
            "The number of items in train is: \n",
            "25\n",
            "The loss for epoch 6\n",
            "0.7844193714857102\n",
            "3\n",
            "Stopping model now\n",
            "Data saved to: \n",
            "28_May_202006_36PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183644-vid0ihm1/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183644-vid0ihm1/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_36PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  408.607574\n",
            "66  2020-04-22  sub_region  ...             66  408.763275\n",
            "67  2020-04-23  sub_region  ...             67  408.592804\n",
            "68  2020-04-24  sub_region  ...             68  408.707031\n",
            "69  2020-04-25  sub_region  ...             69  408.915588\n",
            "70  2020-04-26  sub_region  ...             70  408.787262\n",
            "71  2020-04-27  sub_region  ...             71  409.198151\n",
            "72  2020-04-28  sub_region  ...             72  408.587250\n",
            "73  2020-04-29  sub_region  ...             73  408.494568\n",
            "74  2020-04-30  sub_region  ...             74  408.603363\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183644-vid0ihm1/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183644-vid0ihm1/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: vid0ihm1 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183644-vid0ihm1/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183644-vid0ihm1/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183644-vid0ihm1/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183644-vid0ihm1/media/plotly/test_plot_all_15_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183644-vid0ihm1/media/plotly/test_plot_14_755375b7.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183644-vid0ihm1/media\n",
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            "INFO:wandb.wandb_agent:Cleaning up finished run: vid0ihm1\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
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            "\tuse_mask: True\n",
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            "wandb: Agent Starting Run: hes2lsqt with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: hes2lsqt\n"
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            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
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              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/hes2lsqt\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/hes2lsqt</a><br/>\n",
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          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
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            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
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        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183701-hes2lsqt/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183701-hes2lsqt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183701-hes2lsqt/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183701-hes2lsqt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183701-hes2lsqt/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183701-hes2lsqt/requirements.txt\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.80333824455738\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 0\n",
            "0.8251390935232242\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183701-hes2lsqt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183701-hes2lsqt/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.39603229612112\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.93316801233838\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183701-hes2lsqt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183701-hes2lsqt/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.272497054189444\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.5946873772578934\n",
            "1\n",
            "The running loss is:\n",
            "14.552330110222101\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 3\n",
            "0.6063470879259208\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183701-hes2lsqt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183701-hes2lsqt/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.988487225025892\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 4\n",
            "0.5828536343760788\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['hes2lsqt']\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183701-hes2lsqt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183701-hes2lsqt/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.61138915270567\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n",
            "0.6088078813627362\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183701-hes2lsqt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183701-hes2lsqt/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.06570915877819\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 6\n",
            "0.5444045482824246\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183701-hes2lsqt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183701-hes2lsqt/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "10.021730467677116\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 7\n",
            "0.41757210281987983\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183701-hes2lsqt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183701-hes2lsqt/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "11.953084118664265\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 8\n",
            "0.49804517161101103\n",
            "1\n",
            "The running loss is:\n",
            "10.495671391487122\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 9\n",
            "0.4373196413119634\n",
            "2\n",
            "Data saved to: \n",
            "28_May_202006_37PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183701-hes2lsqt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183701-hes2lsqt/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_37PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  705.102661\n",
            "66  2020-04-22  sub_region  ...             66  705.103027\n",
            "67  2020-04-23  sub_region  ...             67  705.096924\n",
            "68  2020-04-24  sub_region  ...             68  705.107422\n",
            "69  2020-04-25  sub_region  ...             69  705.110229\n",
            "70  2020-04-26  sub_region  ...             70  705.101257\n",
            "71  2020-04-27  sub_region  ...             71  705.131958\n",
            "72  2020-04-28  sub_region  ...             72  705.105957\n",
            "73  2020-04-29  sub_region  ...             73  705.100586\n",
            "74  2020-04-30  sub_region  ...             74  705.097656\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183701-hes2lsqt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183701-hes2lsqt/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: hes2lsqt \n",
            "\n"
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          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183701-hes2lsqt/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183701-hes2lsqt/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183701-hes2lsqt/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183701-hes2lsqt/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183701-hes2lsqt/media/plotly/test_plot_20_2b3d8713.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183701-hes2lsqt/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183701-hes2lsqt/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183701-hes2lsqt/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: hes2lsqt\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: e6va3wtf with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 3\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: e6va3wtf\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/e6va3wtf\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/e6va3wtf</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/e6va3wtf\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmU2dmEzd3RmOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 3\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 3\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 3\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 3\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/media/graph/graph_0_summary_25ec0b50.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.914355665445328\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 0\n",
            "0.8297648193935553\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.440757513046265\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.9350315630435944\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.276884399354458\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.5948701833064357\n",
            "1\n",
            "The running loss is:\n",
            "14.543040201067924\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 3\n",
            "0.6059600083778302\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/wandb-summary.json\n",
            "INFO:wandb.wandb_agent:Running runs: ['e6va3wtf']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.132904570549726\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 4\n",
            "0.5888710237729052\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "13.135652396827936\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n",
            "0.5473188498678306\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.404533512890339\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 6\n",
            "0.5168555630370975\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "12.101209737360477\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 7\n",
            "0.5042170723900199\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "14.456808343529701\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 8\n",
            "0.6023670143137375\n",
            "1\n",
            "The running loss is:\n",
            "8.79074577242136\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 9\n",
            "0.36628107385089\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_37PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_37PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/config.yaml\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  748.224915\n",
            "66  2020-04-22  sub_region  ...             66  748.223572\n",
            "67  2020-04-23  sub_region  ...             67  748.228333\n",
            "68  2020-04-24  sub_region  ...             68  748.199829\n",
            "69  2020-04-25  sub_region  ...             69  748.202820\n",
            "70  2020-04-26  sub_region  ...             70  748.207642\n",
            "71  2020-04-27  sub_region  ...             71  748.156372\n",
            "72  2020-04-28  sub_region  ...             72  748.215271\n",
            "73  2020-04-29  sub_region  ...             73  748.225708\n",
            "74  2020-04-30  sub_region  ...             74  748.218994\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/media/plotly/test_plot_20_62c51e74.plotly.json\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: e6va3wtf \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/media/plotly\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183719-e6va3wtf/wandb-metadata.json\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: e6va3wtf\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: 31vcsdvd with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: True\n",
            "wandb: Agent Started Run: 31vcsdvd\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/31vcsdvd\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/31vcsdvd</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
            "DEBUG:git.cmd:Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=None)\n",
            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/31vcsdvd\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOjMxdmNzZHZkOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 15\n",
            "    use_mask: true\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: true\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/media/graph/graph_0_summary_1f56b7c7.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.587218046188354\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 0\n",
            "0.9411340852578481\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.35363259911537\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.8064013582964739\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.189658485352993\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.6745691035563747\n",
            "The running loss is:\n",
            "15.560340829193592\n",
            "The number of items in train is: \n",
            "24\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The loss for epoch 3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0.648347534549733\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['31vcsdvd']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.733900420367718\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 4\n",
            "0.6555791841819882\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.708370111882687\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n",
            "0.6545154213284453\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.470614947378635\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 6\n",
            "0.6446089561407765\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.394039362668991\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 7\n",
            "0.6414183067778746\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.352656692266464\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 8\n",
            "0.639694028844436\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/wandb-history.jsonl\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.739529885351658\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 9\n",
            "0.6558137452229857\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202006_37PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_37PM_model.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/config.yaml\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  404.308929\n",
            "66  2020-04-22  sub_region  ...             66  405.262390\n",
            "67  2020-04-23  sub_region  ...             67  405.003235\n",
            "68  2020-04-24  sub_region  ...             68  406.038116\n",
            "69  2020-04-25  sub_region  ...             69  403.401398\n",
            "70  2020-04-26  sub_region  ...             70  406.035461\n",
            "71  2020-04-27  sub_region  ...             71  402.491180\n",
            "72  2020-04-28  sub_region  ...             72  404.122070\n",
            "73  2020-04-29  sub_region  ...             73  404.753174\n",
            "74  2020-04-30  sub_region  ...             74  405.485504\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/config.yaml\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mplexporter/exporter.py:84: UserWarning:\n",
            "\n",
            "Blended transforms not yet supported. Zoom behavior may not work as expected.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/mpltools.py:368: MatplotlibDeprecationWarning:\n",
            "\n",
            "\n",
            "The is_frame_like function was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/media/plotly/test_plot_20_dcd65458.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/media/plotly\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:410: UserWarning:\n",
            "\n",
            "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/plotly/matplotlylib/renderer.py:512: UserWarning:\n",
            "\n",
            "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Finished Run: 31vcsdvd \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:shutting down system stats and metadata service\n",
            "INFO:wandb.run_manager:stopping streaming files and file change observer\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/media/plotly/test_plot_all_21_b4a9c644.plotly.json\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183742-31vcsdvd/media/plotly\n",
            "INFO:wandb.wandb_agent:Cleaning up finished run: 31vcsdvd\n",
            "INFO:wandb.wandb_agent:Agent received command: run\n",
            "INFO:wandb.wandb_agent:Agent starting run with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "DEBUG:wandb.wandb_config:wandb dir not provided, skipping defaults\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "wandb: Agent Starting Run: kvnf68wm with config:\n",
            "\tbatch_size: 2\n",
            "\tforecast_history: 15\n",
            "\tlr: 0.0001\n",
            "\tnumber_encoder_layers: 3\n",
            "\tout_seq_length: 4\n",
            "\tuse_mask: False\n",
            "wandb: Agent Started Run: kvnf68wm\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:wandb.wandb_config:no defaults not found in config-defaults.yaml\n"
          ],
          "name": "stderr"
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        {
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              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/covid/covid_forecast\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast</a><br/>\n",
              "                Sweep page: <a href=\"https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/sweeps/65vo8egg</a><br/>\n",
              "Run page: <a href=\"https://app.wandb.ai/covid/covid_forecast/runs/kvnf68wm\" target=\"_blank\">https://app.wandb.ai/covid/covid_forecast/runs/kvnf68wm</a><br/>\n",
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            "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/content/github_aistream-peelout_flow-forecast, universal_newlines=False, shell=None, istream=<valid stream>)\n",
            "DEBUG:wandb.meta:probe for git information\n",
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            "DEBUG:wandb.run_manager:Initialized sync for covid_forecast/kvnf68wm\n",
            "INFO:wandb.run_manager:system metrics and metadata threads started\n",
            "INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:resuming run from id: UnVuOnYxOmt2bmY2OHdtOmNvdmlkX2ZvcmVjYXN0OmNvdmlk\n",
            "INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds\n",
            "INFO:wandb.run_manager:saving pip packages\n",
            "INFO:wandb.run_manager:initializing streaming files api\n",
            "INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "sucessfully deleted layers\n",
            "Weights sucessfully loaded\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n",
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Using Wandb config:\n",
            "wandb_version: 1\n",
            "\n",
            "GCS:\n",
            "  desc: null\n",
            "  value: true\n",
            "_wandb:\n",
            "  desc: null\n",
            "  value:\n",
            "    cli_version: 0.8.36\n",
            "    framework: torch\n",
            "    is_jupyter_run: true\n",
            "    is_kaggle_kernel: false\n",
            "    python_version: 3.6.9\n",
            "batch_size:\n",
            "  desc: null\n",
            "  value: 2\n",
            "dataset_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    class: default\n",
            "    forecast_history: 15\n",
            "    forecast_length: 4\n",
            "    interpolate: false\n",
            "    relevant_cols:\n",
            "    - new_cases\n",
            "    - month\n",
            "    - weekday\n",
            "    - mobility_retail_recreation\n",
            "    - mobility_grocery_pharmacy\n",
            "    - mobility_parks\n",
            "    - mobility_transit_stations\n",
            "    - mobility_workplaces\n",
            "    - mobility_residential\n",
            "    scaler: StandardScaler\n",
            "    target_col:\n",
            "    - new_cases\n",
            "    test_path: United_States__California__Los_Angeles_County.csv\n",
            "    train_end: 67\n",
            "    training_path: United_States__California__Los_Angeles_County.csv\n",
            "    valid_end: 86\n",
            "    valid_start: 68\n",
            "    validation_path: United_States__California__Los_Angeles_County.csv\n",
            "early_stopping:\n",
            "  desc: null\n",
            "  value:\n",
            "    patience: 3\n",
            "forecast_history:\n",
            "  desc: null\n",
            "  value: 15\n",
            "forward_params:\n",
            "  desc: null\n",
            "  value: {}\n",
            "inference_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    dataset_params:\n",
            "      file_path: United_States__California__Los_Angeles_County.csv\n",
            "      forecast_history: 15\n",
            "      forecast_length: 4\n",
            "      interpolate_param: false\n",
            "      relevant_cols:\n",
            "      - new_cases\n",
            "      - month\n",
            "      - weekday\n",
            "      - mobility_retail_recreation\n",
            "      - mobility_grocery_pharmacy\n",
            "      - mobility_parks\n",
            "      - mobility_transit_stations\n",
            "      - mobility_workplaces\n",
            "      - mobility_residential\n",
            "      scaling: StandardScaler\n",
            "      target_col:\n",
            "      - new_cases\n",
            "    datetime_start: '2020-04-21'\n",
            "    decoder_params:\n",
            "      decoder_function: simple_decode\n",
            "      unsqueeze_dim: 1\n",
            "    hours_to_forecast: 10\n",
            "    test_csv_path: United_States__California__Los_Angeles_County.csv\n",
            "lr:\n",
            "  desc: null\n",
            "  value: 0.0001\n",
            "metrics:\n",
            "  desc: null\n",
            "  value:\n",
            "  - MSE\n",
            "model_name:\n",
            "  desc: null\n",
            "  value: CustomTransformerDecoder\n",
            "model_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    n_layers_encoder: 3\n",
            "    n_time_series: 9\n",
            "    output_seq_length: 4\n",
            "    seq_length: 15\n",
            "    use_mask: false\n",
            "model_type:\n",
            "  desc: null\n",
            "  value: PyTorch\n",
            "number_encoder_layers:\n",
            "  desc: null\n",
            "  value: 3\n",
            "out_seq_length:\n",
            "  desc: null\n",
            "  value: 4\n",
            "sweep:\n",
            "  desc: null\n",
            "  value: true\n",
            "training_params:\n",
            "  desc: null\n",
            "  value:\n",
            "    batch_size: 2\n",
            "    criterion: MSE\n",
            "    epochs: 10\n",
            "    lr: 0.0001\n",
            "    optim_params: {}\n",
            "    optimizer: Adam\n",
            "use_mask:\n",
            "  desc: null\n",
            "  value: false\n",
            "wandb:\n",
            "  desc: null\n",
            "  value: false\n",
            "weight_path:\n",
            "  desc: null\n",
            "  value: 25_May_202010_29PM_model.pth\n",
            "weight_path_add:\n",
            "  desc: null\n",
            "  value:\n",
            "    excluded_layers:\n",
            "    - out_length_lay.weight\n",
            "    - out_length_lay.bias\n",
            "    - dense_shape.weight\n",
            "    - dense_shape.bias\n",
            "\n",
            "Torch is using cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183804-kvnf68wm/config.yaml\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183804-kvnf68wm/wandb-metadata.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183804-kvnf68wm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183804-kvnf68wm/media/graph/graph_0_summary_0966bf99.graph.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183804-kvnf68wm/wandb-events.jsonl\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183804-kvnf68wm/wandb-summary.json\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183804-kvnf68wm/requirements.txt\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183804-kvnf68wm/media\n",
            "INFO:wandb.run_manager:file/dir created: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183804-kvnf68wm/media/graph\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "22.53992810845375\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 0\n",
            "0.939163671185573\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183804-kvnf68wm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183804-kvnf68wm/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "19.258116833865643\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 1\n",
            "0.8024215347444018\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183804-kvnf68wm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183804-kvnf68wm/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "16.27618619799614\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 2\n",
            "0.6781744249165058\n",
            "The running loss is:\n",
            "15.538168050348759\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 3\n",
            "0.6474236687645316\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183804-kvnf68wm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183804-kvnf68wm/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.75120346993208\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 4\n",
            "0.6563001445805033\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.wandb_agent:Running runs: ['kvnf68wm']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183804-kvnf68wm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183804-kvnf68wm/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.720529459416866\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 5\n",
            "0.6550220608090361\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183804-kvnf68wm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183804-kvnf68wm/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.435486644506454\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 6\n",
            "0.6431452768544356\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183804-kvnf68wm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183804-kvnf68wm/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.386884167790413\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 7\n",
            "0.6411201736579338\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183804-kvnf68wm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183804-kvnf68wm/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The running loss is:\n",
            "15.28485395014286\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 8\n",
            "0.6368689145892859\n",
            "The running loss is:\n",
            "15.743594877421856\n",
            "The number of items in train is: \n",
            "24\n",
            "The loss for epoch 9\n",
            "0.6559831198925773\n",
            "1\n",
            "Data saved to: \n",
            "28_May_202006_38PM.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183804-kvnf68wm/wandb-history.jsonl\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183804-kvnf68wm/wandb-summary.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Data saved to: \n",
            "28_May_202006_38PM_model.pth\n",
            "interpolate should be below\n",
            "Now loading and scaling United_States__California__Los_Angeles_County.csv\n",
            "CSV Path below\n",
            "United_States__California__Los_Angeles_County.csv\n",
            "torch.Size([1, 15, 9])\n",
            "Add debugging crap below\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:96: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'] = 0\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:97: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor.numpy().tolist()\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:1042: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._set_with(key, value)\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:59: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df, end_tensor, forecast_history, junk, test_data, prediction_samples = infer_on_torch_model(model, **inference_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10])\n",
            "test_data scale\n",
            "Un-transforming data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/evaluator.py:67: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['preds'][history_length:] = end_tensor_list\n",
            "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_acc = evaluate_model(trained_model, model_type, params[\"dataset_params\"][\"target_col\"], params[\"metrics\"], params[\"inference_params\"], {})\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Current historical dataframe\n",
            "          date       level  ... original_index       preds\n",
            "50  2020-04-06  sub_region  ...             50    0.000000\n",
            "51  2020-04-07  sub_region  ...             51    0.000000\n",
            "52  2020-04-08  sub_region  ...             52    0.000000\n",
            "53  2020-04-09  sub_region  ...             53    0.000000\n",
            "54  2020-04-10  sub_region  ...             54    0.000000\n",
            "55  2020-04-11  sub_region  ...             55    0.000000\n",
            "56  2020-04-12  sub_region  ...             56    0.000000\n",
            "57  2020-04-13  sub_region  ...             57    0.000000\n",
            "58  2020-04-14  sub_region  ...             58    0.000000\n",
            "59  2020-04-15  sub_region  ...             59    0.000000\n",
            "60  2020-04-16  sub_region  ...             60    0.000000\n",
            "61  2020-04-17  sub_region  ...             61    0.000000\n",
            "62  2020-04-18  sub_region  ...             62    0.000000\n",
            "63  2020-04-19  sub_region  ...             63    0.000000\n",
            "64  2020-04-20  sub_region  ...             64    0.000000\n",
            "65  2020-04-21  sub_region  ...             65  400.785828\n",
            "66  2020-04-22  sub_region  ...             66  401.776733\n",
            "67  2020-04-23  sub_region  ...             67  401.619812\n",
            "68  2020-04-24  sub_region  ...             68  403.111847\n",
            "69  2020-04-25  sub_region  ...             69  400.459259\n",
            "70  2020-04-26  sub_region  ...             70  402.953522\n",
            "71  2020-04-27  sub_region  ...             71  400.775208\n",
            "72  2020-04-28  sub_region  ...             72  400.355286\n",
            "73  2020-04-29  sub_region  ...             73  400.579224\n",
            "74  2020-04-30  sub_region  ...             74  401.702911\n",
            "\n",
            "[25 rows x 28 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183804-kvnf68wm/config.yaml\n",
            "INFO:wandb.run_manager:file/dir modified: /content/github_aistream-peelout_flow-forecast/wandb/run-20200528_183804-kvnf68wm/wandb-summary.json\n",
            "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Buffered data was truncated after reaching the output size limit."
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5IMgSfe4oIsW",
        "colab_type": "code",
        "outputId": "10a864ad-c648-4131-a129-23404fb8446d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        }
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>level</th>\n",
              "      <th>country</th>\n",
              "      <th>region</th>\n",
              "      <th>sub_region</th>\n",
              "      <th>date</th>\n",
              "      <th>lat</th>\n",
              "      <th>long</th>\n",
              "      <th>cases</th>\n",
              "      <th>deaths</th>\n",
              "      <th>recovered</th>\n",
              "      <th>active</th>\n",
              "      <th>tested</th>\n",
              "      <th>hospitalized</th>\n",
              "      <th>discharged</th>\n",
              "      <th>mobility_retail_recreation</th>\n",
              "      <th>mobility_grocery_pharmacy</th>\n",
              "      <th>mobility_parks</th>\n",
              "      <th>mobility_transit_stations</th>\n",
              "      <th>mobility_workplaces</th>\n",
              "      <th>mobility_residential</th>\n",
              "      <th>full_county</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>164400</th>\n",
              "      <td>sub_region</td>\n",
              "      <td>United States</td>\n",
              "      <td>North Dakota</td>\n",
              "      <td>United States__North Dakota__Grand Forks County</td>\n",
              "      <td>2020-03-29</td>\n",
              "      <td>47.9335</td>\n",
              "      <td>-97.3975</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>400</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-46.0</td>\n",
              "      <td>-20.0</td>\n",
              "      <td>-65.0</td>\n",
              "      <td>-38.0</td>\n",
              "      <td>-28.0</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>North Dakota_Grand Forks County</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164401</th>\n",
              "      <td>sub_region</td>\n",
              "      <td>United States</td>\n",
              "      <td>Georgia</td>\n",
              "      <td>United States__Georgia__Wilcox County</td>\n",
              "      <td>2020-04-13</td>\n",
              "      <td>31.9890</td>\n",
              "      <td>-83.3945</td>\n",
              "      <td>24</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-30.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Georgia_Wilcox County</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164402</th>\n",
              "      <td>sub_region</td>\n",
              "      <td>United States</td>\n",
              "      <td>Colorado</td>\n",
              "      <td>United States__Colorado__Routt County</td>\n",
              "      <td>2020-03-23</td>\n",
              "      <td>40.4610</td>\n",
              "      <td>-107.0345</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-71.0</td>\n",
              "      <td>-34.0</td>\n",
              "      <td>-44.0</td>\n",
              "      <td>-65.0</td>\n",
              "      <td>-51.0</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>Colorado_Routt County</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164403</th>\n",
              "      <td>sub_region</td>\n",
              "      <td>United States</td>\n",
              "      <td>California</td>\n",
              "      <td>United States__California__Mono County</td>\n",
              "      <td>2020-04-08</td>\n",
              "      <td>38.0880</td>\n",
              "      <td>-118.7410</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>103</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-100.0</td>\n",
              "      <td>-68.0</td>\n",
              "      <td>-63.0</td>\n",
              "      <td>-69.0</td>\n",
              "      <td>-57.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>California_Mono County</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164404</th>\n",
              "      <td>sub_region</td>\n",
              "      <td>United States</td>\n",
              "      <td>Florida</td>\n",
              "      <td>United States__Florida__St. Johns County</td>\n",
              "      <td>2020-03-17</td>\n",
              "      <td>29.9375</td>\n",
              "      <td>-81.4515</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>-30.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>Florida_St. Johns County</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             level  ...                      full_county\n",
              "164400  sub_region  ...  North Dakota_Grand Forks County\n",
              "164401  sub_region  ...            Georgia_Wilcox County\n",
              "164402  sub_region  ...            Colorado_Routt County\n",
              "164403  sub_region  ...           California_Mono County\n",
              "164404  sub_region  ...         Florida_St. Johns County\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PzdNBhlPntHQ",
        "colab_type": "code",
        "outputId": "38a9de48-4431-4b9f-9258-3fece4a3d9c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        }
      },
      "source": [
        "df[df['sub_region']==['United_States__California__Los_Angeles_County']]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-37-0667b5d60a9c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sub_region'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'United_States__California__Los_Angeles_County'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/ops/common.py\u001b[0m in \u001b[0;36mnew_method\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0mother\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mitem_from_zerodim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mnew_method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/ops/__init__.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    527\u001b[0m         \u001b[0mrvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextract_numpy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 529\u001b[0;31m         \u001b[0mres_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcomparison_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    530\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    531\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_construct_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mres_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/ops/array_ops.py\u001b[0m in \u001b[0;36mcomparison_op\u001b[0;34m(left, right, op)\u001b[0m\n\u001b[1;32m    232\u001b[0m         \u001b[0;31m#  The ambiguous case is object-dtype.  See GH#27803\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 234\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Lengths must match to compare\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mshould_extension_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Lengths must match to compare"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AwgkzP6ui3Ss",
        "colab_type": "text"
      },
      "source": [
        "### Pre-training on all counties\n",
        "In order to make all the weights transferable we will need to redfine our Wandb sweep."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CE8kWEqti5Ed",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "wandb_sweep_config_transfer = {\n",
        "  \"name\": \"Default sweep\",\n",
        "  \"method\": \"grid\",\n",
        "  \"parameters\": {\n",
        "        \"batch_size\": {\n",
        "            \"values\": [2, 3, 4, 5]\n",
        "        },\n",
        "        \"lr\":{\n",
        "            \"values\":[0.001, 0.002, 0.004, 0.01]\n",
        "        },\n",
        "        \"forecast_history\":{\n",
        "            \"values\":[10, 11, 12]\n",
        "        },\n",
        "        \"out_seq_length\":{\n",
        "            \"values\":[1, 2, 3, 4]\n",
        "        }\n",
        "    }\n",
        "}\n",
        "\n",
        "def get_most_recent_file(file_path):\n",
        "  list_of_files = glob.glob(file_path+\"/*.pth\") # * means all if need specific format then *.csv\n",
        "  if len(list_of_files) > 1:\n",
        "    latest_file = max(list_of_files, key=os.path.getctime)\n",
        "    return latest_file\n",
        "  return None\n",
        "\n",
        "def format_corona_data(region_df:pd.DataFrame, region_name:str):\n",
        "  \"\"\"\n",
        "  Format data for a specific region into \n",
        "  a format that can be used with flow forecast. \n",
        "  \"\"\"\n",
        "  if region_name == 'county':\n",
        "    region_name = region_df['full_county'].iloc[0]\n",
        "  elif region_name=='state':\n",
        "    region_name = region_df['state'].iloc[0]\n",
        "  #else:\n",
        "    #region_name = region_df['country'].iloc[0]\n",
        "  region_df['month'] = pd.to_datetime(region_df['date']).map(lambda x: x.month)\n",
        "  d = pd.to_datetime(region_df['date'])\n",
        "  region_df['weekday'] = d.map(lambda x: x.weekday())\n",
        "  region_df['datetime'] = region_df.date\n",
        "  region_df.index = region_df.date\n",
        "  region_df = region_df.sort_index()\n",
        "  region_df = region_df.fillna(0)\n",
        "  region_df['new_cases'] = region_df['cases'].diff()\n",
        "  region_df.iloc[0]['new_cases'] = 0\n",
        "  region_df= region_df.fillna(0)\n",
        "  region_df.to_csv(region_name+\".csv\")\n",
        "  print(region_df.head(9))\n",
        "  return region_df, len(region_df), region_name+\".csv\"\n",
        "\n",
        "\n",
        "def run_full_geo_code(df_list, start_index, end_index, use_transfer=True):\n",
        "  for i in range(start_index, end_index):\n",
        "    file_path, len_df, file_path_name = format_corona_data(df_list[i], 'county')\n",
        "    latest_file = get_most_recent_file(\"model_save\")\n",
        "    sweep_full = wandb.sweep(wandb_sweep_config_transfer, project=\"covid_forecast\", entity='covid')\n",
        "    if use_transfer and len(os.listdir(\"model_save\"))>1:\n",
        "      print(\"using transfer\")\n",
        "      wandb.agent(sweep_full, lambda:train_function(\"PyTorch\", make_config_file(file_path_name, len_df, weight_path=latest_file)))\n",
        "    else:\n",
        "      wandb.agent(sweep_full, lambda:train_function(\"PyTorch\", make_config_file(file_path_name, len_df)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NGotemtI_Mxf",
        "colab_type": "text"
      },
      "source": [
        "The purpose of this experiment is mainly to tune the MultiHead weights"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PnDwz4Dt2wZN",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Svz3dl9cXS1R",
        "colab_type": "text"
      },
      "source": [
        "## Using fine-tuned weights"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z5CdY2DIXU0r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}